{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,11]],"date-time":"2026-03-11T01:15:20Z","timestamp":1773191720891,"version":"3.50.1"},"reference-count":171,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2024,10,10]],"date-time":"2024-10-10T00:00:00Z","timestamp":1728518400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,10,10]],"date-time":"2024-10-10T00:00:00Z","timestamp":1728518400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int J Multimed Info Retr"],"published-print":{"date-parts":[[2024,12]]},"DOI":"10.1007\/s13735-024-00349-1","type":"journal-article","created":{"date-parts":[[2024,10,10]],"date-time":"2024-10-10T09:01:50Z","timestamp":1728550910000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Recent trends in recommender systems: a survey"],"prefix":"10.1007","volume":"13","author":[{"given":"Chintoo","family":"Kumar","sequence":"first","affiliation":[]},{"given":"C. Ravindranath","family":"Chowdary","sequence":"additional","affiliation":[]},{"given":"Ashok Kumar","family":"Meena","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,10,10]]},"reference":[{"key":"349_CR1","first-page":"757","volume-title":"Proceedings of 2nd international conference on communication, computing and networking, Singapore","author":"SP Singh","year":"2019","unstructured":"Singh SP, Solanki S (2019) Recommender system survey: clustering to nature inspired algorithm. In: Krishna CR, Dutta M, Kumar R (eds) Proceedings of 2nd international conference on communication, computing and networking, Singapore. Springer, Singapore, pp 757\u2013768"},{"key":"349_CR2","unstructured":"Vartak M, Thiagarajan A, Miranda C, Bratman J, Larochelle H (2017) A meta-learning perspective on cold-start recommendations for items. NIPS\u201917, pp 6907\u20136917"},{"issue":"1","key":"349_CR3","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s10844-019-00552-1","volume":"53","author":"AM Aytekin","year":"2019","unstructured":"Aytekin AM, Aytekin T (2019) Real-time recommendation with locality sensitive hashing. J Intell Inf Syst 53(1):1\u201326","journal-title":"J Intell Inf Syst"},{"key":"349_CR4","doi-asserted-by":"crossref","unstructured":"Semerci O, Gruson A, Edwards C, Lacker B, Gibson C, Radosavljevic V (2019) Homepage personalization at spotify. In: Proceedings of the 13th ACM conference on recommender systems, RecSys\u201919, New York, NY, USA. Association for Computing Machinery, pp 527","DOI":"10.1145\/3298689.3346977"},{"key":"349_CR5","doi-asserted-by":"crossref","unstructured":"Gomez-Uribe Carlos A, Hunt Neil (2016) The Netflix recommender system: algorithms, business value, and innovation. ACM Trans Manag Inf Syst 6(4)","DOI":"10.1145\/2843948"},{"key":"349_CR6","doi-asserted-by":"crossref","unstructured":"Karbhari N, Deshmukh A, Shinde VD (2017) Recommendation system using content filtering: a case study for college campus placement. In: 2017 International conference on energy, communication, data analytics and soft computing (ICECDS), pp 963\u2013965","DOI":"10.1109\/ICECDS.2017.8389579"},{"key":"349_CR7","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.knosys.2018.05.001","volume":"157","author":"W Donghui","year":"2018","unstructured":"Donghui W, Yanchun L, Dong X, Xiaoyue F, Renchu G (2018) A content-based recommender system for computer science publications. Knowl-Based Syst 157:1\u20139","journal-title":"Knowl-Based Syst"},{"issue":"12","key":"349_CR8","doi-asserted-by":"publisher","first-page":"61","DOI":"10.1145\/138859.138867","volume":"35","author":"D Goldberg","year":"1992","unstructured":"Goldberg D, Nichols D, Oki BM, Terry D (1992) Using collaborative filtering to weave an information tapestry. Commun ACM 35(12):61\u201370","journal-title":"Commun ACM"},{"key":"349_CR9","doi-asserted-by":"crossref","unstructured":"Schwarz M, Lobur M, Stekh Y (2017) Analysis of the effectiveness of similarity measures for recommender systems. In: 2017 14th International conference the experience of designing and application of cad systems in microelectronics (CADSM). IEEE, pp 275\u2013277","DOI":"10.1109\/CADSM.2017.7916133"},{"issue":"2","key":"349_CR10","doi-asserted-by":"publisher","first-page":"128","DOI":"10.26599\/BDMA.2018.9020012","volume":"1","author":"C Zhang","year":"2018","unstructured":"Zhang C, Yang M, Lv J, Yang W (2018) An improved hybrid collaborative filtering algorithm based on tags and time factor. Big Data Min Anal 1(2):128\u2013136","journal-title":"Big Data Min Anal"},{"key":"349_CR11","doi-asserted-by":"crossref","unstructured":"Yu Z, Lian J, Mahmoody A, Liu G, Xie X (2019) Adaptive user modeling with long and short-term preferences for personalized recommendation. In: Proceedings of the 28th international joint conference on artificial intelligence, IJCAI\u201919. AAAI Press, pp 4213\u20134219","DOI":"10.24963\/ijcai.2019\/585"},{"issue":"6","key":"349_CR12","doi-asserted-by":"publisher","first-page":"734","DOI":"10.1109\/TKDE.2005.99","volume":"17","author":"G Adomavicius","year":"2005","unstructured":"Adomavicius G, Tuzhilin A (2005) Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions. IEEE Trans Knowl Data Eng 17(6):734\u2013749","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"349_CR13","doi-asserted-by":"crossref","unstructured":"Al-Ghossein M, Abdessalem T, Anthony B (2021) A survey on stream-based recommender systems. ACM Comput Surv 54(5)","DOI":"10.1145\/3453443"},{"key":"349_CR14","doi-asserted-by":"crossref","unstructured":"Jannach D, Manzoor A, Cai W, Chen L (2021) A survey on conversational recommender systems. ACM Comput Surv 54(5)","DOI":"10.1145\/3453154"},{"key":"349_CR15","doi-asserted-by":"publisher","first-page":"154","DOI":"10.1016\/j.knosys.2017.02.009","volume":"123","author":"K Matev\u017e","year":"2017","unstructured":"Matev\u017e K, Toma\u017e P (2017) Diversity in recommender systems-a survey. Knowl-Based Syst 123:154\u2013162","journal-title":"Knowl-Based Syst"},{"issue":"5","key":"349_CR16","doi-asserted-by":"publisher","first-page":"813","DOI":"10.1007\/s13042-017-0762-9","volume":"10","author":"S Thiago","year":"2019","unstructured":"Thiago S, Min Z, Xiao L, Yiqun L, Shaoping M (2019) How good your recommender system is? a survey on evaluations in recommendation. Int J Mach Learn Cybern 10(5):813\u2013831","journal-title":"Int J Mach Learn Cybern"},{"issue":"1","key":"349_CR17","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s10115-018-1254-2","volume":"62","author":"S Monika","year":"2020","unstructured":"Monika S (2020) Scalability and sparsity issues in recommender datasets: a survey. Knowl Inf Syst 62(1):1\u201343","journal-title":"Knowl Inf Syst"},{"issue":"4","key":"349_CR18","doi-asserted-by":"publisher","first-page":"305","DOI":"10.1007\/s00799-015-0156-0","volume":"17","author":"B Joeran","year":"2016","unstructured":"Joeran B, Bela G, Stefan L, Corinna B (2016) Research-paper recommender systems: a literature survey. Int J Digit Libr 17(4):305\u2013338","journal-title":"Int J Digit Libr"},{"key":"349_CR19","doi-asserted-by":"crossref","unstructured":"Ding Z, Li X, Jiang C, Zhou M (2018) Objectives and state-of-the-art of location-based social network recommender systems. ACM Comput Surv 51(1)","DOI":"10.1145\/3154526"},{"key":"349_CR20","doi-asserted-by":"publisher","first-page":"430","DOI":"10.1007\/s11704-018-8052-6","volume":"14","author":"Z Guijuan","year":"2020","unstructured":"Guijuan Z, Yang L, Xiaoning J (2020) A survey of autoencoder-based recommender systems. Front Comp Sci 14:430\u2013450","journal-title":"Front Comp Sci"},{"key":"349_CR21","doi-asserted-by":"crossref","unstructured":"Zhang Y, Chen X (2020) Explainable recommendation: a survey and new perspectives, vol 14","DOI":"10.1561\/9781680836592"},{"key":"349_CR22","doi-asserted-by":"publisher","first-page":"271","DOI":"10.1007\/s10844-018-0542-3","volume":"54","author":"S Dara","year":"2019","unstructured":"Dara S, Chowdary CR, Kumar C (2019) A survey on group recommender systems. J Intell Inf Syst 54:271\u2013295","journal-title":"J Intell Inf Syst"},{"key":"349_CR23","doi-asserted-by":"crossref","unstructured":"Deldjoo Y, Noia TD, Merra FA (2021) A survey on adversarial recommender systems: from attack\/defense strategies to generative adversarial networks. ACM Comput Surv 54(2)","DOI":"10.1145\/3439729"},{"key":"349_CR24","doi-asserted-by":"crossref","unstructured":"Dhelim S, Aung N, Bouras MA, Ning H, Cambria E (2021) A survey on personality-aware recommendation systems. Artif Intell Rev","DOI":"10.1007\/s10462-021-10063-7"},{"key":"349_CR25","doi-asserted-by":"crossref","unstructured":"Zhang S, Yao L, Sun A, Tay Y (2019) Deep learning based recommender system: a survey and new perspectives. ACM Comput Surv 52(1)","DOI":"10.1145\/3285029"},{"key":"349_CR26","doi-asserted-by":"publisher","first-page":"485","DOI":"10.1016\/j.knosys.2018.09.011","volume":"163","author":"Y Xiaofeng","year":"2019","unstructured":"Xiaofeng Y, Lixin H, Subin Q, Guoxia X, Hong Y (2019) Singular value decomposition based recommendation using imputed data. Knowl-Based Syst 163:485\u2013494","journal-title":"Knowl-Based Syst"},{"key":"349_CR27","doi-asserted-by":"publisher","first-page":"27668","DOI":"10.1109\/ACCESS.2017.2772226","volume":"5","author":"X Guan","year":"2017","unstructured":"Guan X, Li C, Guan Y (2017) Matrix factorization with rating completion: an enhanced SVD model for collaborative filtering recommender systems. IEEE Access 5:27668\u201327678","journal-title":"IEEE Access"},{"key":"349_CR28","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2019.07.035","volume":"183","author":"Z Shun","year":"2019","unstructured":"Shun Z, Laixiang L, Zhili C, Hong Z (2019) Probabilistic matrix factorization with personalized differential privacy. Knowl-Based Syst 183:104864","journal-title":"Knowl-Based Syst"},{"key":"349_CR29","doi-asserted-by":"publisher","first-page":"177","DOI":"10.1016\/j.knosys.2016.11.021","volume":"118","author":"Z Fuzhi","year":"2017","unstructured":"Fuzhi Z, Yuanli L, Jianmin C, Shaoshuai L, Zhoujun L (2017) Robust collaborative filtering based on non-negative matrix factorization and r1-norm. Knowl-Based Syst 118:177\u2013190","journal-title":"Knowl-Based Syst"},{"key":"349_CR30","doi-asserted-by":"publisher","first-page":"3549","DOI":"10.1109\/ACCESS.2017.2788138","volume":"6","author":"J Bobadilla","year":"2018","unstructured":"Bobadilla J, Bojorque R, Hernando Esteban A, Hurtado R (2018) Recommender systems clustering using Bayesian non negative matrix factorization. IEEE Access 6:3549\u20133564","journal-title":"IEEE Access"},{"key":"349_CR31","doi-asserted-by":"crossref","unstructured":"Koren Y (2008) Factorization meets the neighborhood: a multifaceted collaborative filtering model. In: Proceedings of the 14th ACM SIGKDD international conference on knowledge discovery and data mining, KDD\u201908, New York. ACM, pp 426\u2013434","DOI":"10.1145\/1401890.1401944"},{"issue":"8","key":"349_CR32","first-page":"30","volume":"42","author":"K Yehuda","year":"2009","unstructured":"Yehuda K, Robert B, Chris V (2009) Matrix factorization techniques for recommender systems. Computer 42(8):30\u201337","journal-title":"Computer"},{"key":"349_CR33","doi-asserted-by":"crossref","unstructured":"Hu Y, Koren Y, Volinsky C (2008) Collaborative filtering for implicit feedback datasets. In: 8th IEEE international conference on data mining, 2008. ICDM\u201908. IEEE, pp 263\u2013272","DOI":"10.1109\/ICDM.2008.22"},{"issue":"1","key":"349_CR34","first-page":"442","volume":"17","author":"L Joonseok","year":"2016","unstructured":"Joonseok L, Seungyeon K, Guy L, Yoram S, Samy B (2016) Llorma: local low-rank matrix approximation. J Mach Learn Res 17(1):442\u2013465","journal-title":"J Mach Learn Res"},{"key":"349_CR35","doi-asserted-by":"crossref","unstructured":"Ma W, Wu Y, Gong M, Qin C, Wang S (2017) Local probabilistic matrix factorization for personal recommendation. In: 2017 13th International conference on computational intelligence and security (CIS), pp 97\u2013101","DOI":"10.1109\/CIS.2017.00029"},{"key":"349_CR36","doi-asserted-by":"publisher","first-page":"46","DOI":"10.1016\/j.knosys.2018.01.003","volume":"145","author":"W Hao","year":"2018","unstructured":"Hao W, Zhengxin Z, Kun Y, Binbin Z, Jun H, Liangchen S (2018) Dual-regularized matrix factorization with deep neural networks for recommender systems. Knowl-Based Syst 145:46\u201358","journal-title":"Knowl-Based Syst"},{"issue":"4","key":"349_CR37","doi-asserted-by":"publisher","first-page":"1856","DOI":"10.1109\/TKDE.2020.2998218","volume":"34","author":"J Shuai","year":"2022","unstructured":"Shuai J, Li Kan X, Da RY (2022) Magnitude bounded matrix factorisation for recommender systems. IEEE Trans Knowl Data Eng 34(4):1856\u20131869","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"349_CR38","doi-asserted-by":"crossref","unstructured":"Cao D, He X, Miao L, An Y, Yang C, Hong R (2018) Attentive group recommendation. In: The 41st international ACM SIGIR conference on research & development in information retrieval, SIGIR\u201918, New York. ACM, pp 645\u2013654","DOI":"10.1145\/3209978.3209998"},{"key":"349_CR39","doi-asserted-by":"crossref","unstructured":"Sankar A, Wu Y, Wu Y, Zhang W, Yang H, Sundaram H (2020) Groupim: a mutual information maximization framework for neural group recommendation. In: Proceedings of the 43rd international acm sigir conference on research and development in information retrieval, SIGIR\u201920, New York, NY, USA. Association for Computing Machinery, pp 1279\u20131288","DOI":"10.1145\/3397271.3401116"},{"key":"349_CR40","doi-asserted-by":"publisher","first-page":"110","DOI":"10.1016\/j.knosys.2019.03.026","volume":"176","author":"DK Chae","year":"2019","unstructured":"Chae DK, Kim SW, Lee JT (2019) Autoencoder-based personalized ranking framework unifying explicit and implicit feedback for accurate top-n recommendation. Knowl-Based Syst 176:110\u2013121","journal-title":"Knowl-Based Syst"},{"key":"349_CR41","doi-asserted-by":"crossref","unstructured":"Wang H, Wang N, Yeung DY (2015) Collaborative deep learning for recommender systems. In Cao L, Zhang C, Joachims T, Webb GI, Margineantu DD, Williams G (eds) Proceedings of the 21th ACM SIGKDD international conference on knowledge discovery and data mining, Sydney, NSW, Australia, Aug 10\u201313, 2015. ACM, pp 1235\u20131244","DOI":"10.1145\/2783258.2783273"},{"key":"349_CR42","doi-asserted-by":"crossref","unstructured":"He X, Liao L, Zhang H, Nie L, Hu X, Chua T-S (2017) Neural collaborative filtering. In: Proceedings of the 26th international conference on world wide web, WWW\u201917, International world wide web conferences steering committee, Republic and Canton of Geneva, Switzerland, pp 173\u2013182","DOI":"10.1145\/3038912.3052569"},{"key":"349_CR43","doi-asserted-by":"crossref","unstructured":"He X, Chua T-S (2017) Neural factorization machines for sparse predictive analytics. In: Kando N, Sakai T, Joho H, Li H, de Vries AP, White RW (eds) Proceedings of the 40th international ACM SIGIR conference on research and development in information retrieval, Shinjuku, Tokyo, Japan, Aug 7\u201311, 2017. ACM, Berlib, pp 355\u2013364","DOI":"10.1145\/3077136.3080777"},{"key":"349_CR44","doi-asserted-by":"crossref","unstructured":"He X, Du X, Wang X, Tian F, Tang J, Chua T-S (2018) Outer product-based neural collaborative filtering. IJCAI\u201918. AAAI Press, pp 2227\u20132233","DOI":"10.24963\/ijcai.2018\/308"},{"key":"349_CR45","doi-asserted-by":"crossref","unstructured":"Shan Y, Hoens TR, Jiao J, Wang H, Yu D, Mao JC (2016) Deep crossing: Web-scale modeling without manually crafted combinatorial features. In: Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, KDD\u201916, New York, NY, USA. Association for Computing Machinery, pp 255\u2013262","DOI":"10.1145\/2939672.2939704"},{"key":"349_CR46","doi-asserted-by":"crossref","unstructured":"Xue H-J, Dai X-Y, Zhang J, Huang S, Chen J (2017) Deep matrix factorization models for recommender systems. In: Proceedings of the 26th international joint conference on artificial intelligence, IJCAI\u201917. AAAI Press, pp 3203-3209","DOI":"10.24963\/ijcai.2017\/447"},{"key":"349_CR47","doi-asserted-by":"crossref","unstructured":"Covington P, Adams J, Sargin E (2016) Deep neural networks for youtube recommendations. In: Proceedings of the 10th ACM conference on recommender systems, RecSys\u201916, New York. Association for Computing Machinery, pp 191\u2013198","DOI":"10.1145\/2959100.2959190"},{"key":"349_CR48","doi-asserted-by":"crossref","unstructured":"Chen J, Zhang H, He X, Nie L, Liu W, Chua T-S (2017) Attentive collaborative filtering: Multimedia recommendation with item- and component-level attention. In: Proceedings of the 40th international ACM SIGIR conference on research and development in information retrieval, SIGIR\u201917, New York, NY, USA. Association for Computing Machinery, pp 335\u2013344","DOI":"10.1145\/3077136.3080797"},{"key":"349_CR49","doi-asserted-by":"crossref","unstructured":"Cheng H-T, Koc L, Harmsen J, Shaked T, Chandra T, Aradhye H, Anderson G, Corrado G, Chai W, Ispir M, Anil R, Haque Z, Hong L, Jain V, Liu X, Shah H (2016) Wide & deep learning for recommender systems. In: Proceedings of the 1st workshop on deep learning for recommender systems, DLRS 2016, New York, NY, USA. Association for Computing Machinery, pp 7\u201310","DOI":"10.1145\/2988450.2988454"},{"issue":"12","key":"349_CR50","doi-asserted-by":"publisher","first-page":"2354","DOI":"10.1109\/TKDE.2018.2831682","volume":"30","author":"X He","year":"2018","unstructured":"He X, He Z, Song J, Liu Z, Jiang Y-G, Chua T-S (2018) Nais: neural attentive item similarity model for recommendation. IEEE Trans Knowl Data Eng 30(12):2354\u20132366","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"349_CR51","doi-asserted-by":"crossref","unstructured":"Lian J, Zhou X, Zhang F, Chen Z, Xie X, Sun G (2018) Xdeepfm: combining explicit and implicit feature interactions for recommender systems. In: Proceedings of the 24th ACM SIGKDD international conference on knowledge discovery & data mining, KDD\u201918, New York, NY, USA. Association for Computing Machinery, pp 1754\u20131763","DOI":"10.1145\/3219819.3220023"},{"key":"349_CR52","doi-asserted-by":"crossref","unstructured":"Xue F, He X, Wang X, Xu J, Kai L, Hong R (2019) Deep item-based collaborative filtering for top-n recommendation. ACM Trans Inf Syst 37(3)","DOI":"10.1145\/3314578"},{"key":"349_CR53","doi-asserted-by":"crossref","unstructured":"Guo H, Tang R, Ye Y, Li Z, He X (2017) Deepfm: a factorization-machine based neural network for ctr prediction. In: Proceedings of the 26th international joint conference on artificial intelligence, IJCAI\u201917. AAAI Press, pp 1725\u20131731","DOI":"10.24963\/ijcai.2017\/239"},{"issue":"11","key":"349_CR54","doi-asserted-by":"publisher","first-page":"9235","DOI":"10.1007\/s13369-019-03946-z","volume":"44","author":"Z Batmaz","year":"2019","unstructured":"Batmaz Z, Kaleli C (2019) AE-MCCF: an autoencoder-based multi-criteria recommendation algorithm. Arab J Sci Eng 44(11):9235\u20139247","journal-title":"Arab J Sci Eng"},{"key":"349_CR55","doi-asserted-by":"crossref","unstructured":"Qu Y, Fang B, Zhang W, Tang R, Niu M, Guo H, Yong Y, He X (2018) Product-based neural networks for user response prediction over multi-field categorical data. ACM Trans Inf Syst 37(1)","DOI":"10.1145\/3233770"},{"key":"349_CR56","doi-asserted-by":"crossref","unstructured":"Chae D-K, Kang J-S, Kim S-W, Lee J-T (2018) CFGAN: a generic collaborative filtering framework based on generative adversarial networks. In: Proceedings of the 27th ACM international conference on information and knowledge management, pp 137\u2013146","DOI":"10.1145\/3269206.3271743"},{"key":"349_CR57","doi-asserted-by":"crossref","unstructured":"Wang R, Fu B, Fu G, Wang M (2017) Deep & cross network for ad click predictions. In: Proceedings of the ADKDD\u201917, ADKDD\u201917, New York, NY, USA. Association for Computing Machinery","DOI":"10.1145\/3124749.3124754"},{"key":"349_CR58","doi-asserted-by":"crossref","unstructured":"Tang J, Wang K (2018) Personalized top-n sequential recommendation via convolutional sequence embedding. WSDM\u201918, New York, NY, USA. Association for Computing Machinery, pp 565\u2013573","DOI":"10.1145\/3159652.3159656"},{"key":"349_CR59","doi-asserted-by":"crossref","unstructured":"Wang H, Zhang F, Xie X, Guo M (2018) Dkn: Deep knowledge-aware network for news recommendation. In: Proceedings of the 2018 World Wide Web Conference, WWW \u201918, Republic and Canton of Geneva, CHE. International World Wide Web Conferences Steering Committee, pp 1835\u20131844","DOI":"10.1145\/3178876.3186175"},{"key":"349_CR60","doi-asserted-by":"crossref","unstructured":"An M, Wu F, Wu C, Zhang K, Liu Z, Xie X (2019) Neural news recommendation with long- and short-term user representations. In: Proceedings of the 57th annual meeting of the association for computational linguistics, Florence, Italy. Association for Computational Linguistics, pp 336\u2013345","DOI":"10.18653\/v1\/P19-1033"},{"key":"349_CR61","doi-asserted-by":"crossref","unstructured":"Ahamed M\u00a0T, Afroge S (2019) A recommender system based on deep neural network and matrix factorization for collaborative filtering. In 2019 International Conference on Electrical, Computer and Communication Engineering (ECCE), pages 1\u20135","DOI":"10.1109\/ECACE.2019.8679125"},{"key":"349_CR62","doi-asserted-by":"crossref","unstructured":"Bai T, Wen J-R, Zhang J, Zhao WX (2017) A neural collaborative filtering model with interaction-based neighborhood. CIKM\u201917, New York, NY, USA. Association for Computing Machinery, pp 1979\u20131982","DOI":"10.1145\/3132847.3133083"},{"key":"349_CR63","doi-asserted-by":"crossref","unstructured":"Hsieh C-K, Yang L, Cui Y, Lin T-Y, Belongie S, Estrin D (2017) Collaborative metric learning. In: Proceedings of the 26th international conference on world wide web, WWW\u201917, Republic and Canton of Geneva, CHE. International World Wide Web Conferences Steering Committee, pp 193\u2013201","DOI":"10.1145\/3038912.3052639"},{"key":"349_CR64","doi-asserted-by":"crossref","unstructured":"Sedhain S, Menon AK, Sanner S, Xie L (2015) Autorec: autoencoders meet collaborative filtering. In Proceedings of the 24th international conference on world wide web, WWW\u201915 Companion, New York, NY, USA. Association for Computing Machinery, pp 111\u2013112","DOI":"10.1145\/2740908.2742726"},{"key":"349_CR65","doi-asserted-by":"crossref","unstructured":"Wu Y, DuBois C, Zheng AX, Ester M (2016) Collaborative denoising auto-encoders for top-n recommender systems. WSDM\u201916, New York, NY, USA. Association for Computing Machinery, pp 153\u2013162","DOI":"10.1145\/2835776.2835837"},{"key":"349_CR66","doi-asserted-by":"crossref","unstructured":"Chen CM, Wang CJ, Tsai MF, Yang YH (2019) Collaborative similarity embedding for recommender systems. In: Liu L, White RW, Mantrach A, Silvestri F, McAuley JJ, Baeza-Yates R, Zia L (eds) The World Wide Web Conference, WWW 2019, San Francisco, CA, USA, May 13\u201317, 2019. ACM, pp 2637\u20132643","DOI":"10.1145\/3308558.3313493"},{"key":"349_CR67","doi-asserted-by":"crossref","unstructured":"Yang J-H, Chen C-M, Wang C-J, Tsai M-F (2018) Hop-rec: high-order proximity for implicit recommendation. In: Proceedings of the 12th ACM conference on recommender systems, RecSys\u201918, New York, NY, USA. Association for Computing Machinery, pp 140\u2013144","DOI":"10.1145\/3240323.3240381"},{"key":"349_CR68","unstructured":"Kuchaiev O, Ginsburg B (2017) Training deep autoencoders for collaborative filtering. arXiv preprint arXiv:1708.01715"},{"key":"349_CR69","doi-asserted-by":"publisher","first-page":"164","DOI":"10.1016\/j.cogsys.2019.01.011","volume":"55","author":"W Kai","year":"2019","unstructured":"Kai W, Lei X, Ling H, Chang-Dong W, Jian-Huang L (2019) Sddrs: stacked discriminative denoising auto-encoder based recommender system. Cogn Syst Res 55:164\u2013174","journal-title":"Cogn Syst Res"},{"key":"349_CR70","doi-asserted-by":"crossref","unstructured":"Jiang M, Yang Z, Zhao C (2017) What to play next? A RNN-based music recommendation system. In: 2017 51st Asilomar conference on signals, systems, and computers. IEEE, pp 356\u2013358","DOI":"10.1109\/ACSSC.2017.8335200"},{"key":"349_CR71","doi-asserted-by":"crossref","unstructured":"Quadrana M, Karatzoglou A, Hidasi B, Cremonesi P (2017) Personalizing session-based recommendations with hierarchical recurrent neural networks. In: Proceedings of the 11th ACM conference on recommender systems, pp 130\u2013137","DOI":"10.1145\/3109859.3109896"},{"key":"349_CR72","doi-asserted-by":"crossref","unstructured":"Devooght R, Bersini H (2017) Long and short-term recommendations with recurrent neural networks. In: Proceedings of the 25th conference on user modeling, adaptation and personalization, pp 13\u201321","DOI":"10.1145\/3079628.3079670"},{"key":"349_CR73","doi-asserted-by":"crossref","unstructured":"Jannach D, Ludewig M (2017) When recurrent neural networks meet the neighborhood for session-based recommendation. In: Proceedings of the 11th ACM conference on recommender systems, pp 306\u2013310","DOI":"10.1145\/3109859.3109872"},{"key":"349_CR74","doi-asserted-by":"crossref","unstructured":"Suglia A, Greco C, Musto C, De\u00a0Gemmis M, Lops P, Semeraro G (2017) A deep architecture for content-based recommendations exploiting recurrent neural networks. In: Proceedings of the 25th conference on user modeling, adaptation and personalization, pp 202\u2013211","DOI":"10.1145\/3079628.3079684"},{"key":"349_CR75","doi-asserted-by":"crossref","unstructured":"Zheng L, Noroozi V, Yu PS (2017) Joint deep modeling of users and items using reviews for recommendation. In: Proceedings of the Tenth ACM international conference on web search and data mining, WSDM\u201917, New York, NY, USA. Association for Computing Machinery, pp 425\u2013434","DOI":"10.1145\/3018661.3018665"},{"key":"349_CR76","doi-asserted-by":"crossref","unstructured":"Chen C, Zhang M, Liu Y, Ma S (2018) Neural attentional rating regression with review-level explanations. In: Proceedings of the 2018 world wide web conference, WWW\u201918, Republic and Canton of Geneva, CHE. International World Wide Web Conferences Steering Committee, pp 1583\u20131592","DOI":"10.1145\/3178876.3186070"},{"key":"349_CR77","doi-asserted-by":"crossref","unstructured":"Tuinhof H, Pirker C, Haltmeier M (2018) Image-based fashion product recommendation with deep learning. In: International conference on machine learning, optimization, and data science. Springer, pp 472\u2013481","DOI":"10.1007\/978-3-030-13709-0_40"},{"key":"349_CR78","doi-asserted-by":"crossref","unstructured":"Wang S, Wang Y, Tang J, Shu K, Ranganath S, Liu H (2017) What your images reveal: exploiting visual contents for point-of-interest recommendation. In: Proceedings of the 26th international conference on world wide web, WWW\u201917, Republic and Canton of Geneva, CHE. International World Wide Web Conferences Steering Committee, pp 391\u2013400","DOI":"10.1145\/3038912.3052638"},{"key":"349_CR79","doi-asserted-by":"crossref","unstructured":"Rawat YS, Kankanhalli MS (2016) Contagnet: exploiting user context for image tag recommendation. In: Proceedings of the 24th ACM international conference on multimedia, pp 1102\u20131106","DOI":"10.1145\/2964284.2984068"},{"key":"349_CR80","doi-asserted-by":"crossref","unstructured":"Zhou X, Li Y, Liang W (2020) CNN-RNN based intelligent recommendation for online medical pre-diagnosis support. IEEE\/ACM Trans Comput Biol Bioinformat","DOI":"10.1109\/TCBB.2020.2994780"},{"key":"349_CR81","doi-asserted-by":"crossref","unstructured":"Hirotsu T, Hirota M, Araki T, Endo M, Ishikawa H (2019) Tourism application with CNN-based classification specialized for cultural information. In: Proceedings of the 21st international conference on information integration and web-based applications & services, pp 8\u201314","DOI":"10.1145\/3366030.3366073"},{"key":"349_CR82","doi-asserted-by":"crossref","unstructured":"Zheng L, Noroozi V, Yu PS (2017) Joint deep modeling of users and items using reviews for recommendation. In: Proceedings of the 10th ACM international conference on web search and data mining, WSDM\u201917, New York, NY, USA. Association for Computing Machinery, pp 425\u2013434","DOI":"10.1145\/3018661.3018665"},{"key":"349_CR83","doi-asserted-by":"crossref","unstructured":"Su C, Chen M, Xie X (2021) Graph convolutional matrix completion via relation reconstruction. In: 2021 10th International conference on software and computer applications, ICSCA 2021, New York, NY, USA. Association for Computing Machinery, pp 51\u201356","DOI":"10.1145\/3457784.3457792"},{"key":"349_CR84","doi-asserted-by":"crossref","unstructured":"Salakhutdinov R, Mnih A, Hinton G (2007) Restricted Boltzmann machines for collaborative filtering. In: Proceedings of the 24th international conference on machine learning, pp 791\u2013798","DOI":"10.1145\/1273496.1273596"},{"key":"349_CR85","doi-asserted-by":"publisher","first-page":"197","DOI":"10.1007\/s12927-017-0010-6","volume":"9","author":"VT Le","year":"2017","unstructured":"Le VT (2017) Qos prediction for web services based on restricted Boltzmann machines. J Serv Sci Res 9:197\u2013217","journal-title":"J Serv Sci Res"},{"issue":"8","key":"349_CR86","doi-asserted-by":"publisher","first-page":"3999","DOI":"10.1007\/s00521-019-04106-y","volume":"32","author":"N Tran Son","year":"2020","unstructured":"Tran Son N, Son N, d\u2019Avila GA (2020) Probabilistic approaches for music similarity using restricted Boltzmann machines. Neural Comput Appl 32(8):3999\u20134008","journal-title":"Neural Comput Appl"},{"key":"349_CR87","doi-asserted-by":"crossref","unstructured":"Li C, Li J (2017) Collaborative filtering based on dual conditional restricted Boltzmann machines. In: 2017 36th Chinese control conference (CCC). IEEE, pp 10871\u201310874","DOI":"10.23919\/ChiCC.2017.8029090"},{"key":"349_CR88","doi-asserted-by":"crossref","unstructured":"Zheng G, Zhang F, Zheng Z, Xiang Y, Yuan NJ, Xie X, Li Z (2018) DRN: a deep reinforcement learning framework for news recommendation. In: Proceedings of the 2018 world wide web conference, pp 167\u2013176","DOI":"10.1145\/3178876.3185994"},{"key":"349_CR89","doi-asserted-by":"crossref","unstructured":"Zhao X, Xia L, Zhang L, Ding Z, Yin D, Tang J (2018) Deep reinforcement learning for page-wise recommendations. In: Proceedings of the 12th ACM conference on recommender systems, pp 95\u2013103","DOI":"10.1145\/3240323.3240374"},{"key":"349_CR90","doi-asserted-by":"crossref","unstructured":"Zhao X, Xia L, Zou L, Liu H, Yin D, Tang J (2020) Whole-chain recommendations. In: Proceedings of the 29th ACM international conference on information & knowledge management, pp 1883\u20131891","DOI":"10.1145\/3340531.3412044"},{"key":"349_CR91","unstructured":"Goodfellow IJ, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S, Courville A, Bengio Y (2014) Generative adversarial nets. In: Proceedings of the 27th international conference on neural information processing systems, NIPS\u201914, vol 2, Cambridge, MA, USA. MIT Press, pp 2672\u20132680"},{"key":"349_CR92","doi-asserted-by":"crossref","unstructured":"Wang J, Yu L, Zhang W, Gong Y, Xu Y, Wang B, Zhang P, Zhang D (2017) Irgan: a minimax game for unifying generative and discriminative information retrieval models. In: Proceedings of the 40th international ACM SIGIR conference on research and development in information retrieval, pp 515\u2013524","DOI":"10.1145\/3077136.3080786"},{"key":"349_CR93","doi-asserted-by":"crossref","unstructured":"He X, He Z, Du X, Chua T-S (2018) Adversarial personalized ranking for recommendation. In: The 41st international ACM SIGIR conference on research & development in information retrieval, SIGIR\u201918, New York, NY, USA. Association for Computing Machinery, pp 355\u2013364","DOI":"10.1145\/3209978.3209981"},{"key":"349_CR94","doi-asserted-by":"crossref","unstructured":"Deldjoo Y, Di\u00a0Noia T, Merra FA (2020) Adversarial machine learning in recommender systems (aml-recsys). In: Proceedings of the 13th international conference on web search and data mining, pp 869\u2013872","DOI":"10.1145\/3336191.3371877"},{"issue":"8","key":"349_CR95","first-page":"2731","volume":"31","author":"W Cheng","year":"2019","unstructured":"Cheng W, Mathias N, Hui L (2019) Recsys-dan: discriminative adversarial networks for cross-domain recommender systems. IEEE Trans Neural Netw Learn Syst 31(8):2731\u20132740","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"349_CR96","doi-asserted-by":"crossref","unstructured":"Bharadhwaj H, Park H, Lim BY (2018) Recgan: recurrent generative adversarial networks for recommendation systems. In: Proceedings of the 12th ACM conference on recommender systems, pp 372\u2013376","DOI":"10.1145\/3240323.3240383"},{"key":"349_CR97","doi-asserted-by":"crossref","unstructured":"Ying R, He R, Chen K, Eksombatchai P, Hamilton WL, Leskovec J (2018) Graph convolutional neural networks for web-scale recommender systems. In: Guo Y, Farooq F (eds) Proceedings of the 24th ACM SIGKDD international conference on knowledge discovery & data Mining, KDD 2018, London, UK, Aug 19\u201323, 2018. ACM, pp 974\u2013983","DOI":"10.1145\/3219819.3219890"},{"key":"349_CR98","unstructured":"Jain A, Liu I, Sarda A, Molino P (2019) Using graph learning to power recommendations, Food discovery with UBER eats"},{"key":"#cr-split#-349_CR99.1","doi-asserted-by":"crossref","unstructured":"Yu B, Yin H, Zhu Z (2018) Spatio-temporal graph convolutional networks: a deep learning framework for traffic forecasting. In: J Lang","DOI":"10.24963\/ijcai.2018\/505"},{"key":"#cr-split#-349_CR99.2","unstructured":"(ed) Proceedings of the 27th international joint conference on artificial intelligence, IJCAI 2018, July 13-19, 2018, Stockholm, Sweden, pp 3634-3640. ijcai.org"},{"issue":"9","key":"349_CR100","first-page":"1","volume":"10","author":"T Agagu","year":"2018","unstructured":"Agagu T, Tran T (2018) Context-aware recommendation methods. Int J Intell Syst Appl 10(9):1","journal-title":"Int J Intell Syst Appl"},{"key":"349_CR101","doi-asserted-by":"crossref","unstructured":"Adomavicius G, Tuzhilin A (2011) Context-aware recommender systems. In: Recommender systems handbook. Springer, pp 217\u2013253","DOI":"10.1007\/978-0-387-85820-3_7"},{"key":"349_CR102","doi-asserted-by":"publisher","first-page":"173","DOI":"10.1016\/j.knosys.2017.11.003","volume":"140","author":"M Villegas Norha","year":"2018","unstructured":"Villegas Norha M, Cristian S, Javier D-C, Gabriel T (2018) Characterizing context-aware recommender systems: a systematic literature review. Knowl-Based Syst 140:173\u2013200","journal-title":"Knowl-Based Syst"},{"key":"349_CR103","doi-asserted-by":"publisher","first-page":"84","DOI":"10.1016\/j.cosrev.2019.01.001","volume":"31","author":"R Shaina","year":"2019","unstructured":"Shaina R, Chen D (2019) Progress in context-aware recommender systems-an overview. Comput Sci Rev 31:84\u201397","journal-title":"Comput Sci Rev"},{"issue":"2","key":"349_CR104","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3298988","volume":"37","author":"W Libing","year":"2019","unstructured":"Libing W, Cong Q, Chenliang L, Qian W, Bolong Z, Xiangyang L (2019) A context-aware user-item representation learning for item recommendation. ACM Trans Inf Syst (TOIS) 37(2):1\u201329","journal-title":"ACM Trans Inf Syst (TOIS)"},{"key":"349_CR105","doi-asserted-by":"crossref","unstructured":"Wang X, Chen Y, Yang J, Wu L, Wu Z, Xie X (2018) A reinforcement learning framework for explainable recommendation. In: 2018 IEEE international conference on data mining (ICDM). IEEE, pp 587\u2013596","DOI":"10.1109\/ICDM.2018.00074"},{"key":"349_CR106","doi-asserted-by":"crossref","unstructured":"Zhou Z, Liu S, Xu G, Xie X, Yin J, Li Y, Zhang W (2018) Knowledge-based recommendation with hierarchical collaborative embedding. In: Pacific-Asia conference on knowledge discovery and data mining. Springer, pp 222\u2013234","DOI":"10.1007\/978-3-319-93037-4_18"},{"key":"349_CR107","doi-asserted-by":"publisher","DOI":"10.1016\/j.smhl.2019.100083","volume":"15","author":"A Gyrard","year":"2020","unstructured":"Gyrard A, Sheth A (2020) Iamhappy: towards an iot knowledge-based cross-domain well-being recommendation system for everyday happiness. Smart Health 15:100083","journal-title":"Smart Health"},{"issue":"4","key":"349_CR108","first-page":"2124","volume":"15","author":"RR Lopes","year":"2018","unstructured":"Lopes RR, Maria SG, Vicente RW, Zegarra RD (2018) A knowledge-based recommendation system that includes sentiment analysis and deep learning. IEEE Trans Industr Inf 15(4):2124\u20132135","journal-title":"IEEE Trans Industr Inf"},{"key":"349_CR109","doi-asserted-by":"crossref","unstructured":"Abdollahi B, Nasraoui O (2018) Transparency in fair machine learning: the case of explainable recommender systems. In: Human and machine learning. Springer, pp 21\u201335","DOI":"10.1007\/978-3-319-90403-0_2"},{"key":"349_CR110","doi-asserted-by":"crossref","unstructured":"Kim S, Kang H, Choi S, Kim D, Yang M, Park C (2024) Large language models meet collaborative filtering: an efficient all-round LLM-based recommender system. arXiv preprint arXiv:2404.11343","DOI":"10.1145\/3637528.3671931"},{"key":"349_CR111","doi-asserted-by":"crossref","unstructured":"Balog K, Radlinski F, Arakelyan S (2019) Transparent, scrutable and explainable user models for personalized recommendation. In: Proceedings of the 42nd international ACM Sigir conference on research and development in information retrieval, pp 265\u2013274","DOI":"10.1145\/3331184.3331211"},{"key":"349_CR112","doi-asserted-by":"publisher","first-page":"38","DOI":"10.1016\/j.neucom.2017.02.005","volume":"241","author":"R Xingyi","year":"2017","unstructured":"Xingyi R, Meina S, Haihong E, Junde S (2017) Context-aware probabilistic matrix factorization modeling for point-of-interest recommendation. Neurocomputing 241:38\u201355","journal-title":"Neurocomputing"},{"key":"349_CR113","unstructured":"Sawant S, Pai G (2013) Yelp food recommendation system"},{"key":"349_CR114","doi-asserted-by":"crossref","unstructured":"Cho E, Han Meng (2019) Ai powered book recommendation system. ACM SE\u201919, New York. Association for Computing Machinery, pp 230\u2013232","DOI":"10.1145\/3299815.3314465"},{"key":"349_CR115","unstructured":"Daniyalzade E, Lipus T. Facebook friend suggestion"},{"issue":"1","key":"349_CR116","doi-asserted-by":"publisher","first-page":"76","DOI":"10.1109\/MIC.2003.1167344","volume":"7","author":"G Linden","year":"2003","unstructured":"Linden G, Smith B, York J (2003) Amazon.com recommendations: item-to-item collaborative filtering. IEEE Internet Comput 7(1):76\u201380","journal-title":"IEEE Internet Comput"},{"key":"349_CR117","unstructured":"Ertl O (2017) Superminhash\u2014a new minwise hashing algorithm for jaccard similarity estimation. CoRR, abs\/1706.05698"},{"key":"349_CR118","unstructured":"Wu L, Shah S, Choi S, Tiwari M, Posse C (2014) The browsemaps: collaborative filtering at Linkedin. In: Jannach D, Freyne J, Geyer W, Guy I, Hotho A, Mobasher B (eds) Proceedings of the 6th workshop on recommender systems and the social web (RSWeb 2014) co-located with the 8th ACM conference on recommender systems (RecSys 2014), Foster City, CA, USA, Oct 6, 2014, vol 1271 of CEUR workshop proceedings. CEUR-WS.org"},{"key":"349_CR119","unstructured":"Choumane A, Ibrahim Zein AA (2020) Friend recommendation based on hashtags analysis. CoRR, abs\/2003.03531"},{"key":"349_CR120","doi-asserted-by":"crossref","unstructured":"Vargas S, Hristakeva M, Jack K (2016) Mendeley: recommendations for researchers. In: Proceedings of the 10th ACM conference on recommender systems, RecSys\u201916, New York, NY, USA. Association for Computing Machinery, pp 365","DOI":"10.1145\/2959100.2959116"},{"key":"349_CR121","doi-asserted-by":"crossref","unstructured":"Zhao XW, Guo Y, He Y, Jiang H, Wu Y, Li X (2014) We know what you want to buy: a demographic-based system for product recommendation on microblogs. In: Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining, pp 1935\u20131944","DOI":"10.1145\/2623330.2623351"},{"key":"349_CR122","doi-asserted-by":"crossref","unstructured":"Wang Y, Chan SCF, Ngai G (2012) Applicability of demographic recommender system to tourist attractions: a case study on trip advisor. In: 2012 IEEE\/WIC\/ACM international conferences on web intelligence and intelligent agent technology, vol\u00a03. IEEE, pp 97\u2013101","DOI":"10.1109\/WI-IAT.2012.133"},{"key":"349_CR123","doi-asserted-by":"crossref","unstructured":"Subbotin S, Gladkova O, Parkhomenko A (2018) Knowledge-based recommendation system for embedded systems platform-oriented design. In: 2018 IEEE 13th international scientific and technical conference on computer sciences and information technologies (CSIT), vol 1. IEEE, pp 368\u2013373","DOI":"10.1109\/STC-CSIT.2018.8526659"},{"key":"349_CR124","doi-asserted-by":"publisher","first-page":"103","DOI":"10.1016\/j.inffus.2020.12.001","volume":"69","author":"P Iv\u00e1n","year":"2021","unstructured":"Iv\u00e1n P, Carlos P, Luiz P, Ido G, Enrique H-V (2021) Reciprocal recommender systems: Analysis of state-of-art literature, challenges and opportunities towards social recommendation. Inf Fusion 69:103\u2013127","journal-title":"Inf Fusion"},{"key":"349_CR125","doi-asserted-by":"crossref","unstructured":"Palomares I (2020) Reciprocal recommendation: matching users with the right users. In: Proceedings of the 43rd international ACM SIGIR conference on research and development in information retrieval, SIGIR\u201920, New York. Association for Computing Machinery, pp 2429\u20132431","DOI":"10.1145\/3397271.3401420"},{"key":"349_CR126","doi-asserted-by":"crossref","unstructured":"Kleinerman A, Rosenfeld A, Ricci F, Kraus S (2020) Supporting users in finding successful matches in reciprocal recommender systems. In: User modeling and user-adapted interaction, pp 1\u201349","DOI":"10.1007\/s11257-020-09279-z"},{"key":"349_CR127","doi-asserted-by":"crossref","unstructured":"Neve J, Palomares I (2020) Hybrid reciprocal recommender systems: integrating item-to-user principles in reciprocal recommendation. In: Companion proceedings of the web conference 2020, WWW\u201920, New York. Association for Computing Machinery, pp 848\u2013853","DOI":"10.1145\/3366424.3383295"},{"key":"349_CR128","doi-asserted-by":"crossref","unstructured":"Jameson A (2004) More than the sum of its members: challenges for group recommender systems. In: Proceedings of the working conference on advanced visual interfaces, AVI\u201904. ACM, pp 48\u201354","DOI":"10.1145\/989863.989869"},{"issue":"1","key":"349_CR129","doi-asserted-by":"publisher","first-page":"754","DOI":"10.14778\/1687627.1687713","volume":"2","author":"S Amer-Yahia","year":"2009","unstructured":"Amer-Yahia S, Roy SB, Chawlat A, Das G, Yu C (2009) Group recommendation: semantics and efficiency. Proc VLDB Endow 2(1):754\u2013765","journal-title":"Proc VLDB Endow"},{"key":"349_CR130","doi-asserted-by":"publisher","first-page":"115","DOI":"10.1016\/j.eswa.2017.03.069","volume":"82","author":"A Akshita","year":"2017","unstructured":"Akshita A, Manajit C, Ravindranath Chowdary C (2017) Does order matter? effect of order in group recommendation. Expert Syst Appl 82:115\u2013127","journal-title":"Expert Syst Appl"},{"key":"349_CR131","doi-asserted-by":"crossref","unstructured":"Vinh\u00a0Tran L, Nguyen\u00a0Pham T-A, Tay Y, Liu Y, Cong G, Li X (2019) Interact and decide: medley of sub-attention networks for effective group recommendation. In: Proceedings of the 42Nd international ACM SIGIR conference on research and development in information retrieval, SIGIR\u201919, New York, NY, USA. ACM, pp 255\u2013264","DOI":"10.1145\/3331184.3331251"},{"key":"349_CR132","doi-asserted-by":"crossref","unstructured":"C Kumar, CR Chowdary, D Shukla (2022) Automatically detecting groups using locality-sensitive hashing in group recommendations. Inf Sci 601:207\u2013223","DOI":"10.1016\/j.ins.2022.04.028"},{"key":"349_CR133","first-page":"677","volume-title":"Group recommender systems: combining individual models","author":"J Masthoff","year":"2011","unstructured":"Masthoff J (2011) Group recommender systems: combining individual models. Springer, Boston, pp 677\u2013702"},{"key":"349_CR134","doi-asserted-by":"publisher","unstructured":"Kumar C, Chowdary CR (2023) A study on the role of uninterested items in group recommendations. Electron Commer Res 23:2073\u20132099. https:\/\/doi.org\/10.1007\/s10660-021-09526-4","DOI":"10.1007\/s10660-021-09526-4"},{"key":"349_CR135","doi-asserted-by":"crossref","unstructured":"McCarthy JF, Anagnost TD (1998) Musicfx: an arbiter of group preferences for computer supported collaborative workouts. In: Proceedings of the 1998 ACM conference on computer supported cooperative work, CSCW\u201998, New York. Association for Computing Machinery, pp 363\u2013372","DOI":"10.1145\/289444.289511"},{"key":"349_CR136","unstructured":"McCarthy JF (2002) Pocket restaurant finder: a situated recommender systems for groups. In: Proceeding of workshop on mobile ad-hoc communication at the 2002 ACM conference on human factors in computer systems"},{"key":"349_CR137","doi-asserted-by":"crossref","unstructured":"Crossen A, Budzik J, Hammond KJ (2002) Flytrap: intelligent group music recommendation. In: Proceedings of the 7th international conference on intelligent user interfaces, IUI\u201902, New York. ACM, pp 184\u2013185","DOI":"10.1145\/502743.502748"},{"key":"349_CR138","doi-asserted-by":"crossref","unstructured":"Sotelo R, Blanco Y, Lopez M, Gil A, Pazos J (2009) Tv program recommendiation for groups based on multidimensional tv-anytime classifications. In: 2009 Digest of technical papers international conference on consumer electronics, pp 1\u20132","DOI":"10.1109\/ICCE.2009.5012309"},{"key":"349_CR139","doi-asserted-by":"crossref","unstructured":"Lieberman H, Van\u00a0Dyke NW, Vivacqua AS (1999) Let\u2019s browse: a collaborative web browsing agent. In: Proceedings of the 4th international conference on intelligent user interfaces, IUI\u201999, New York. ACM, pp 65\u201368","DOI":"10.1145\/291080.291092"},{"issue":"9","key":"349_CR140","doi-asserted-by":"publisher","first-page":"3293","DOI":"10.1007\/s10489-019-01455-y","volume":"49","author":"S Dara","year":"2019","unstructured":"Dara S, Chowdary CR (2019) A study on the role of flexible preferences in group recommendations. Appl Intell 49(9):3293\u20133307","journal-title":"Appl Intell"},{"key":"349_CR141","doi-asserted-by":"crossref","unstructured":"Ye M, Liu X, Lee W-C (2012) Exploring social influence for recommendation: a generative model approach. In: Proceedings of the 35th international ACM SIGIR conference on research and development in information retrieval, SIGIR\u201912, New York, NY, USA. Association for Computing Machinery, pp 671\u2013680","DOI":"10.1145\/2348283.2348373"},{"key":"349_CR142","doi-asserted-by":"crossref","unstructured":"Liu X, Tian Y, Ye M, Lee W-C (2012) Exploring personal impact for group recommendation. In: Proceedings of the 21st ACM international conference on information and knowledge management, CIKM\u201912, New York. Association for Computing Machinery, pp 674\u2013683","DOI":"10.1145\/2396761.2396848"},{"key":"349_CR143","doi-asserted-by":"crossref","unstructured":"Yuan Q, Cong G, Lin C-Y (2014) Com: a generative model for group recommendation. In: Proceedings of the 20th ACM SIGKDD international conference on knowledge discovery and data mining, KDD\u201914, New York, NY, USA. Association for Computing Machinery, pp 163\u2013172","DOI":"10.1145\/2623330.2623616"},{"key":"349_CR144","doi-asserted-by":"crossref","unstructured":"He X, Liao L, Zhang H, Nie L, Hu X, Chua T-S (2017) Neural collaborative filtering. In: Proceedings of the 26th international conference on world wide web, WWW\u201917, Republic and Canton of Geneva, Switzerland. International World Wide Web Conferences Steering Committee, pp 173\u2013182","DOI":"10.1145\/3038912.3052569"},{"issue":"7","key":"349_CR145","first-page":"3447","volume":"34","author":"Y Hongzhi","year":"2022","unstructured":"Hongzhi Y, Qinyong W, Kai Z, Zhixu L, Xiaofang Z (2022) Overcoming data sparsity in group recommendation. IEEE Trans Knowl Data Eng 34(7):3447\u20133460","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"349_CR146","doi-asserted-by":"crossref","unstructured":"Yin H, Wang Q, Zheng K, Li Z, Yang J, Zhou X (2019) Social influence-based group representation learning for group recommendation. In: 2019 IEEE 35th international conference on data engineering (ICDE), pp 566\u2013577","DOI":"10.1109\/ICDE.2019.00057"},{"key":"349_CR147","doi-asserted-by":"crossref","unstructured":"Chang S, Zhang Y, Tang J, Yin D, Chang Y, Hasegawa-Johnson MA, Huang TS (2017) Streaming recommender systems. In: Proceedings of the 26th international conference on world wide web, WWW\u201917, Republic and Canton of Geneva, CHE. International World Wide Web Conferences Steering Committee, pp 381\u2013389","DOI":"10.1145\/3038912.3052627"},{"key":"349_CR148","doi-asserted-by":"crossref","unstructured":"Hao Z, Cheng Y, Cai R, Wen W, Wang L (2015) A semi-supervised solution for cold start issue on recommender systems. In: Asia-Pacific web conference. Springer, pp 805\u2013817","DOI":"10.1007\/978-3-319-25255-1_66"},{"key":"349_CR149","unstructured":"Dureddy HV, Kaden Z (2018) Handling cold-start collaborative filtering with reinforcement learning. arXiv preprint arXiv:1806.06192"},{"key":"349_CR150","doi-asserted-by":"publisher","first-page":"59","DOI":"10.1016\/j.ins.2016.10.043","volume":"378","author":"A Gogna","year":"2017","unstructured":"Gogna A, Majumdar A (2017) Diablo: optimization based design for improving diversity in recommender system. Inf Sci 378:59\u201374","journal-title":"Inf Sci"},{"key":"349_CR151","doi-asserted-by":"crossref","unstructured":"Mohamed MH, Khafagy MH, Ibrahim MH (2019) Recommender systems challenges and solutions survey. In: 2019 International conference on innovative trends in computer engineering (ITCE). IEEE, pp 149\u2013155","DOI":"10.1109\/ITCE.2019.8646645"},{"issue":"2","key":"349_CR152","doi-asserted-by":"publisher","first-page":"261","DOI":"10.1007\/s11257-020-09282-4","volume":"31","author":"M-R Joanna","year":"2021","unstructured":"Joanna M-R, Bipin I, Aleksander S-P (2021) Meta-user2vec model for addressing the user and item cold-start problem in recommender systems. User Model User Adapt Interact 31(2):261\u2013286","journal-title":"User Model User Adapt Interact"},{"key":"349_CR153","doi-asserted-by":"crossref","unstructured":"da\u00a0Silva Jo\u00e3o FG, de\u00a0Moura\u00a0Junior NN, Caloba LP (2018) Effects of data sparsity on recommender systems based on collaborative filtering. In: 2018 International joint conference on neural networks (IJCNN). IEEE, pp 1\u20138","DOI":"10.1109\/IJCNN.2018.8489095"},{"key":"349_CR154","doi-asserted-by":"crossref","unstructured":"Kaur P, Goel S (2016) Shilling attack models in recommender system. In: 2016 International conference on inventive computation technologies (ICICT), vol\u00a02. IEEE, pp 1\u20135","DOI":"10.1109\/INVENTIVE.2016.7824865"},{"key":"349_CR155","doi-asserted-by":"crossref","unstructured":"Wang Y, Wu Z, Cao J, Fang C (2012) Towards a tricksy group shilling attack model against recommender systems. In: International conference on advanced data mining and applications. Springer, pp 675\u2013688","DOI":"10.1007\/978-3-642-35527-1_56"},{"issue":"10","key":"349_CR156","doi-asserted-by":"publisher","first-page":"112","DOI":"10.23919\/JCC.2019.10.008","volume":"16","author":"Q Lingtao","year":"2019","unstructured":"Lingtao Q, Haiping H, Feng L, Reza M, Ruchuan W (2019) A novel shilling attack detection model based on particle filter and gravitation. China Commun 16(10):112\u2013132","journal-title":"China Commun"},{"issue":"5","key":"349_CR157","doi-asserted-by":"publisher","first-page":"1189","DOI":"10.1109\/TCSS.2020.3013878","volume":"7","author":"Z Fuzhi","year":"2020","unstructured":"Fuzhi Z, Shilei W (2020) Detecting group shilling attacks in online recommender systems based on bisecting k-means clustering. IEEE Trans Comput Soc Syst 7(5):1189\u20131199","journal-title":"IEEE Trans Comput Soc Syst"},{"key":"349_CR158","doi-asserted-by":"crossref","unstructured":"Kaur B, Rani S (2021) Identification of gray sheep using different clustering algorithms. In: Proceedings of the 2nd international conference on information management and machine intelligence. Springer, pp 211\u2013217","DOI":"10.1007\/978-981-15-9689-6_24"},{"key":"349_CR159","doi-asserted-by":"crossref","unstructured":"Srivastava A, Bala PK, Kumar B (2020) New perspectives on gray sheep behavior in e-commerce recommendations. J Retail Consum Serv 53:101764","DOI":"10.1016\/j.jretconser.2019.02.018"},{"key":"349_CR160","doi-asserted-by":"crossref","unstructured":"Fazziki AE, El\u00a0Aissaoui O, El\u00a0Alami Yasser EM, Allioui YE, Benbrahim M (2019) A new collaborative approach to solve the gray-sheep users problem in recommender systems. In: 2019 3rd International conference on intelligent computing in data sciences (ICDS). IEEE, pp 1\u20134","DOI":"10.1109\/ICDS47004.2019.8942256"},{"key":"349_CR161","doi-asserted-by":"publisher","first-page":"1000","DOI":"10.1016\/j.procs.2016.04.214","volume":"83","author":"P Sasmita","year":"2016","unstructured":"Sasmita P, Lenka Rakesh K, Ananya S (2016) A hybrid distributed collaborative filtering recommender engine using apache spark. Procedia Comput Sci 83:1000\u20131006","journal-title":"Procedia Comput Sci"},{"key":"349_CR162","doi-asserted-by":"publisher","first-page":"509","DOI":"10.1016\/j.compeleceng.2018.02.017","volume":"67","author":"W Ruoxuan","year":"2018","unstructured":"Ruoxuan W, Hui T, Hong S (2018) Improving k-anonymity based privacy preservation for collaborative filtering. Comput Electr Eng 67:509\u2013519","journal-title":"Comput Electr Eng"},{"issue":"9","key":"349_CR163","doi-asserted-by":"publisher","first-page":"1770","DOI":"10.1109\/TKDE.2018.2805356","volume":"30","author":"S Hyejin","year":"2018","unstructured":"Hyejin S, Sungwook K, Junbum S, Xiaokui X (2018) Privacy enhanced matrix factorization for recommendation with local differential privacy. IEEE Trans Knowl Data Eng 30(9):1770\u20131782","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"349_CR164","doi-asserted-by":"publisher","first-page":"145","DOI":"10.1016\/j.jpdc.2017.12.015","volume":"127","author":"Z Feng","year":"2019","unstructured":"Feng Z, Lee Victor E, Ruoming J, Saurabh G, Raymond CK-K, Michele M, Lijun D, Chi C (2019) Privacy-aware smart city: a case study in collaborative filtering recommender systems. J Parallel Distrib Comput 127:145\u2013159","journal-title":"J Parallel Distrib Comput"},{"key":"349_CR165","unstructured":"Anderson C (2006) The long tail: why the future of business is selling less of more. Hachette Books"},{"key":"349_CR166","doi-asserted-by":"crossref","unstructured":"Celma \u00d2 (2010) The long tail in recommender systems. In: Music recommendation and discovery. Springer, pp 87\u2013107","DOI":"10.1007\/978-3-642-13287-2_4"},{"key":"349_CR167","series-title":"Lecture Notes in Computer Science","first-page":"35","volume-title":"Case-based reasoning research and development - 25th International conference, ICCBR 2017, Trondheim, Norway, June 26\u201328, 2017, Proceedings","author":"A Gharbi","year":"2017","unstructured":"Gharbi A, Jorro-Aragoneses Jose L, Stelios K, Miltos P, Recio-Garc\u00eda Juan A, Bel\u00e9n D-A (2017) A hybrid CBR approach for the long tail problem in recommender systems. In: Aha DW, Lieber J (eds) Case-based reasoning research and development - 25th International conference, ICCBR 2017, Trondheim, Norway, June 26\u201328, 2017, Proceedings, vol 10339. Lecture Notes in Computer Science. Springer, pp 35\u201345"},{"key":"349_CR168","doi-asserted-by":"publisher","first-page":"348","DOI":"10.1016\/j.knosys.2018.11.004","volume":"164","author":"Elaheh Malekzadeh Hamedani and Marjan Kaedi","year":"2019","unstructured":"Elaheh Malekzadeh Hamedani and Marjan Kaedi (2019) Recommending the long tail items through personalized diversification. Knowl-Based Syst 164:348\u2013357","journal-title":"Knowl-Based Syst"},{"key":"349_CR169","doi-asserted-by":"crossref","unstructured":"Ferraro A (2019) Music cold-start and long-tail recommendation: bias in deep representations. In: Proceedings of the 13th ACM conference on recommender systems, RecSys\u201919, New York. Association for Computing Machinery, pp 586\u2013590","DOI":"10.1145\/3298689.3347052"},{"key":"349_CR170","doi-asserted-by":"crossref","unstructured":"Liu S, Zheng Y (2020) Long-tail session-based recommendation. In: 14th ACM conference on recommender systems, RecSys\u201920, New York. Association for Computing Machinery, pp 509\u2013514","DOI":"10.1145\/3383313.3412222"}],"container-title":["International Journal of Multimedia Information Retrieval"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13735-024-00349-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s13735-024-00349-1\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13735-024-00349-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,28]],"date-time":"2024-11-28T09:13:03Z","timestamp":1732785183000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s13735-024-00349-1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,10,10]]},"references-count":171,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2024,12]]}},"alternative-id":["349"],"URL":"https:\/\/doi.org\/10.1007\/s13735-024-00349-1","relation":{},"ISSN":["2192-6611","2192-662X"],"issn-type":[{"value":"2192-6611","type":"print"},{"value":"2192-662X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,10,10]]},"assertion":[{"value":"3 May 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"24 August 2024","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"5 September 2024","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"10 October 2024","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"On behalf of all authors, the corresponding author states that there is no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}],"article-number":"41"}}