{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,1]],"date-time":"2026-02-01T04:21:47Z","timestamp":1769919707403,"version":"3.49.0"},"reference-count":73,"publisher":"Springer Science and Business Media LLC","issue":"30","license":[{"start":{"date-parts":[[2023,8,17]],"date-time":"2023-08-17T00:00:00Z","timestamp":1692230400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,8,17]],"date-time":"2023-08-17T00:00:00Z","timestamp":1692230400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100012165","name":"Key Technologies Research and Development Program","doi-asserted-by":"publisher","award":["2021YFF0901705"],"award-info":[{"award-number":["2021YFF0901705"]}],"id":[{"id":"10.13039\/501100012165","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput & Applic"],"published-print":{"date-parts":[[2023,10]]},"DOI":"10.1007\/s00521-023-08940-z","type":"journal-article","created":{"date-parts":[[2023,8,17]],"date-time":"2023-08-17T04:02:33Z","timestamp":1692244953000},"page":"22737-22754","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Multi-component graph collaborative filtering using auxiliary information for TV program recommendation"],"prefix":"10.1007","volume":"35","author":[{"given":"Zebin","family":"Yao","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Meiqi","family":"Ji","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tongtong","family":"Xing","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ruiling","family":"Fu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sitong","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fulian","family":"Yin","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,8,17]]},"reference":[{"key":"8940_CR1","unstructured":"Kampankis P, Kallitsis M, Sridharan S, Devetsikiotis M (2006) Triple play\u2014a survey. Electrical and Computer Engineering Department North Carolina State University, Raleigh, Spring, vol 6 (2006)"},{"key":"8940_CR2","doi-asserted-by":"crossref","unstructured":"Dhage SN, Patil SK, Meshram B (2014) Survey on: interactive video-on-demand (VOD) systems. In: 2014 international conference on circuits, systems, communication and information technology applications (CSCITA), pp 435\u2013440","DOI":"10.1109\/CSCITA.2014.6839300"},{"issue":"4","key":"8940_CR3","first-page":"31","volume":"110","author":"PB Thorat","year":"2015","unstructured":"Thorat PB, Goudar R, Barve S (2015) Survey on collaborative filtering, content-based filtering and hybrid recommendation system. Int J Comput Appl 110(4):31\u201336","journal-title":"Int J Comput Appl"},{"issue":"1","key":"8940_CR4","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/2556270","volume":"47","author":"Y Shi","year":"2014","unstructured":"Shi Y, Larson M, Hanjalic A (2014) Collaborative filtering beyond the user-item matrix: a survey of the state of the art and future challenges. ACM Comput Surv CSUR 47(1):1\u201345","journal-title":"ACM Comput Surv CSUR"},{"key":"8940_CR5","doi-asserted-by":"crossref","unstructured":"Guo Q, Sun Z, Theng Y-L (2019) Exploiting side information for recommendation. In: International conference on web engineering, pp 569\u2013573","DOI":"10.1007\/978-3-030-19274-7_46"},{"key":"8940_CR6","doi-asserted-by":"crossref","unstructured":"Shi Y, Larson M, Hanjalic A (2010) Mining mood-specific movie similarity with matrix factorization for context-aware recommendation. In: Proceedings of the workshop on context-aware movie recommendation, pp 34\u201340","DOI":"10.1145\/1869652.1869658"},{"key":"8940_CR7","doi-asserted-by":"crossref","unstructured":"Sun Z, Yang J, Zhang J, Bozzon A, Chen Y, Xu C (2017) MRLR: multi-level representation learning for personalized ranking in recommendation. In: IJCAI, pp 2807\u20132813","DOI":"10.24963\/ijcai.2017\/391"},{"issue":"8","key":"8940_CR8","doi-asserted-by":"publisher","first-page":"1633","DOI":"10.1109\/TPAMI.2016.2605085","volume":"39","author":"B Yang","year":"2016","unstructured":"Yang B, Lei Y, Liu J, Li W (2016) Social collaborative filtering by trust. IEEE Trans Pattern Anal Mach Intell 39(8):1633\u20131647","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"issue":"5","key":"8940_CR9","doi-asserted-by":"publisher","first-page":"1607","DOI":"10.1007\/s00521-020-05085-1","volume":"33","author":"H Tahmasebi","year":"2021","unstructured":"Tahmasebi H, Ravanmehr R, Mohamadrezaei R (2021) Social movie recommender system based on deep autoencoder network using twitter data. Neural Comput Appl 33(5):1607\u20131623","journal-title":"Neural Comput Appl"},{"key":"8940_CR10","unstructured":"Wu C-Y, Ahmed A, Beutel A, Smola AJ (2017) Joint training of ratings and reviews with recurrent recommender networks. In: 5th international conference on learning representations, ICLR 2017, Toulon, France, April 24\u201326, 2017, workshop track proceedings. OpenReview.net"},{"issue":"6","key":"8940_CR11","doi-asserted-by":"publisher","first-page":"317","DOI":"10.3390\/info11060317","volume":"11","author":"M Srifi","year":"2020","unstructured":"Srifi M, Oussous A, Ait Lahcen A, Mouline S (2020) Recommender systems based on collaborative filtering using review texts\u2014a survey. Information 11(6):317","journal-title":"Information"},{"key":"8940_CR12","doi-asserted-by":"crossref","unstructured":"Liu Q, Wu S, Wang L (2017) DeepStyle: learning user preferences for visual recommendation. In: Proceedings of the 40th international ACM SIGIR conference on research and development in information retrieval, pp 841\u2013844","DOI":"10.1145\/3077136.3080658"},{"key":"8940_CR13","doi-asserted-by":"crossref","unstructured":"Niu W, Caverlee J, Lu H (2018) Neural personalized ranking for image recommendation. In: Proceedings of the eleventh ACM international conference on web search and data mining, pp 423\u2013431","DOI":"10.1145\/3159652.3159728"},{"key":"8940_CR14","doi-asserted-by":"crossref","unstructured":"Moshfeghi Y, Piwowarski B, Jose JM (2011) Handling data sparsity in collaborative filtering using emotion and semantic based features. In: Proceedings of the 34th international ACM SIGIR conference on research and development in information retrieval, pp 625\u2013634 (2011)","DOI":"10.1145\/2009916.2010001"},{"key":"8940_CR15","doi-asserted-by":"crossref","unstructured":"Yang D, Zhang D, Yu Z, Wang Z (2013) A sentiment-enhanced personalized location recommendation system. In: Proceedings of the 24th ACM conference on hypertext and social media, pp 119\u2013128","DOI":"10.1145\/2481492.2481505"},{"issue":"1","key":"8940_CR16","doi-asserted-by":"publisher","first-page":"17","DOI":"10.1109\/TKDE.2016.2598561","volume":"29","author":"C Shi","year":"2016","unstructured":"Shi C, Li Y, Zhang J, Sun Y, Philip SY (2016) A survey of heterogeneous information network analysis. IEEE Trans Knowl Data Eng 29(1):17\u201337","journal-title":"IEEE Trans Knowl Data Eng"},{"issue":"2","key":"8940_CR17","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/978-3-031-01902-9","volume":"3","author":"Y Sun","year":"2012","unstructured":"Sun Y, Han J (2012) Mining heterogeneous information networks: principles and methodologies. Synth Lect Data Min Knowl Discov 3(2):1\u2013159","journal-title":"Synth Lect Data Min Knowl Discov"},{"key":"8940_CR18","doi-asserted-by":"crossref","unstructured":"Chen Y, Wang C (2017) HINE: heterogeneous information network embedding. In: International conference on database systems for advanced applications, pp 180\u2013195","DOI":"10.1007\/978-3-319-55753-3_12"},{"issue":"2","key":"8940_CR19","doi-asserted-by":"publisher","first-page":"357","DOI":"10.1109\/TKDE.2018.2833443","volume":"31","author":"C Shi","year":"2018","unstructured":"Shi C, Hu B, Zhao WX, Philip SY (2018) Heterogeneous information network embedding for recommendation. IEEE Trans Knowl Data Eng 31(2):357\u2013370","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"8940_CR20","unstructured":"Bruna J, Zaremba W, Szlam A, LeCun Y (2013) Spectral networks and locally connected networks on graphs. In: 2nd international conference on learning representations, ICLR 2014, Banff, AB, Canada, April 14\u201316, 2014, conference track proceedings"},{"key":"8940_CR21","unstructured":"Defferrard M, Bresson X, Vandergheynst P (2016) Convolutional neural networks on graphs with fast localized spectral filtering. Adv Neural Inf Process Syst 29"},{"key":"8940_CR22","unstructured":"Kipf TN, Welling M (2016) Semi-supervised classification with graph convolutional networks. In: 5th international conference on learning representations, ICLR 2017, Toulon, France, April 24\u201326, 2017, conference track proceedings"},{"key":"8940_CR23","unstructured":"Rendle S, Freudenthaler C, Gantner Z, Schmidt-Thieme L (2012) BPR: Bayesian personalized ranking from implicit feedback. Computing Research Repository (CoRR)"},{"key":"8940_CR24","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, pp 173\u2013182","DOI":"10.1145\/3038912.3052569"},{"key":"8940_CR25","doi-asserted-by":"crossref","unstructured":"Kabbur S, Ning X, Karypis G (2013) FISM: factored item similarity models for top-N recommender systems. In: Proceedings of the 19th ACM SIGKDD international conference on knowledge discovery and data mining, pp 659\u2013667","DOI":"10.1145\/2487575.2487589"},{"issue":"12","key":"8940_CR26","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"},{"issue":"2","key":"8940_CR27","doi-asserted-by":"publisher","first-page":"179","DOI":"10.1109\/TKDE.2009.85","volume":"22","author":"P Symeonidis","year":"2009","unstructured":"Symeonidis P, Nanopoulos A, Manolopoulos Y (2009) A unified framework for providing recommendations in social tagging systems based on ternary semantic analysis. IEEE Trans Knowl Data Eng 22(2):179\u2013192","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"8940_CR28","doi-asserted-by":"crossref","unstructured":"Rendle S, Schmidt-Thieme L (2010) Pairwise interaction tensor factorization for personalized tag recommendation. In: Proceedings of the third ACM international conference on web search and data mining, pp 81\u201390","DOI":"10.1145\/1718487.1718498"},{"key":"8940_CR29","doi-asserted-by":"crossref","unstructured":"Ifada N, Nayak R (2014) Tensor-based item recommendation using probabilistic ranking in social tagging systems. In: Proceedings of the 23rd international conference on world wide web, pp 805\u2013810","DOI":"10.1145\/2567948.2579243"},{"key":"8940_CR30","doi-asserted-by":"crossref","unstructured":"Ifada N, Nayak R (2015) Do-Rank: DCG optimization for learning-to-rank in tag-based item recommendation systems. In: Pacific-Asia conference on knowledge discovery and data mining. Springer, pp 510\u2013521","DOI":"10.1007\/978-3-319-18032-8_40"},{"key":"8940_CR31","unstructured":"Hamilton W, Ying Z, Leskovec J (2017) Inductive representation learning on large graphs. In: Advances in neural information processing systems 30: annual conference on neural information processing systems 2017, December 4\u20139, 2017, Long Beach, CA, USA, pp 1024\u20131034"},{"key":"8940_CR32","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: Proceedings of the 24th ACM SIGKDD international conference on knowledge discovery & data mining, pp 974\u2013983","DOI":"10.1145\/3219819.3219890"},{"key":"8940_CR33","unstructured":"Berg RVD, Kipf TN, Welling M (2017) Graph convolutional matrix completion. Computing Research Repository (CoRR)"},{"key":"8940_CR34","doi-asserted-by":"crossref","unstructured":"Wang X, He X, Wang M, Feng F, Chua T-S (2019) Neural graph collaborative filtering. In: Proceedings of the 42nd international ACM SIGIR conference on research and development in information retrieval, pp 165\u2013174","DOI":"10.1145\/3331184.3331267"},{"key":"8940_CR35","doi-asserted-by":"crossref","unstructured":"He X, Deng K, Wang X, Li Y, Zhang Y, Wang M (2020) LIGHTGCN: simplifying and powering graph convolution network for recommendation. In: Proceedings of the 43rd international ACM SIGIR conference on research and development in information retrieval, pp 639\u2013648","DOI":"10.1145\/3397271.3401063"},{"key":"8940_CR36","doi-asserted-by":"crossref","unstructured":"Liu F, Cheng Z. Zhu L, Gao Z, Nie L (2021) Interest-aware message-passing gcn for recommendation. In: Proceedings of the web conference 2021, pp 1296\u20131305","DOI":"10.1145\/3442381.3449986"},{"key":"8940_CR37","unstructured":"Ma J, Cui P, Kuang K, Wang X, Zhu W (2019) Disentangled graph convolutional networks. In: International conference on machine learning, pp 4212\u20134221"},{"issue":"11","key":"8940_CR38","doi-asserted-by":"publisher","first-page":"992","DOI":"10.14778\/3402707.3402736","volume":"4","author":"Y Sun","year":"2011","unstructured":"Sun Y, Han J, Yan X, Yu PS, Wu T (2011) PathSim: meta path-based top-k similarity search in heterogeneous information networks. Proc VLDB Endow 4(11):992\u20131003","journal-title":"Proc VLDB Endow"},{"issue":"10","key":"8940_CR39","doi-asserted-by":"publisher","first-page":"2479","DOI":"10.1109\/TKDE.2013.2297920","volume":"26","author":"C Shi","year":"2014","unstructured":"Shi C, Kong X, Huang Y, Philip SY, Wu B (2014) HeteSim: a general framework for relevance measure in heterogeneous networks. IEEE Trans Knowl Data Eng 26(10):2479\u20132492","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"8940_CR40","doi-asserted-by":"crossref","unstructured":"Shi C, Zhou C, Kong X, Yu PS, Liu G, Wang B (2012) HeteRecom: a semantic-based recommendation system in heterogeneous networks. In: Proceedings of the 18th ACM SIGKDD international conference on knowledge discovery and data mining, pp 1552\u20131555","DOI":"10.1145\/2339530.2339778"},{"issue":"1","key":"8940_CR41","doi-asserted-by":"publisher","first-page":"153","DOI":"10.1007\/s11280-018-0553-6","volume":"22","author":"C Shi","year":"2019","unstructured":"Shi C, Zhang Z, Ji Y, Wang W, Yu PS, Shi Z (2019) SemRec: a personalized semantic recommendation method based on weighted heterogeneous information networks. World Wide Web 22(1):153\u2013184","journal-title":"World Wide Web"},{"key":"8940_CR42","doi-asserted-by":"crossref","unstructured":"Jamali M, Lakshmanan L (2013) HeteroMF: recommendation in heterogeneous information networks using context dependent factor models. In: Proceedings of the 22nd international conference on world wide web, pp 643\u2013654","DOI":"10.1145\/2488388.2488445"},{"key":"8940_CR43","unstructured":"Yu X, Ren X, Gu Q, Sun Y, Han J (2013) Collaborative filtering with entity similarity regularization in heterogeneous information networks. IJCAI HINA 27"},{"key":"8940_CR44","doi-asserted-by":"crossref","unstructured":"Luo C, Pang W, Wang Z, Lin C (2014) HETE-CF: social-based collaborative filtering recommendation using heterogeneous relations. In: 2014 IEEE international conference on data mining, pp 917\u2013922","DOI":"10.1109\/ICDM.2014.64"},{"key":"8940_CR45","doi-asserted-by":"crossref","unstructured":"Yu X, Ren X, Sun Y, Sturt B, Khandelwal U, Gu Q, Norick B, Han J (2013) Recommendation in heterogeneous information networks with implicit user feedback. In: Proceedings of the 7th ACM conference on recommender systems, pp 347\u2013350","DOI":"10.1145\/2507157.2507230"},{"key":"8940_CR46","doi-asserted-by":"crossref","unstructured":"Yu X, Ren X, Sun Y, Gu Q, Sturt B, Khandelwal U, Norick B, Han J (2014) Personalized entity recommendation: a heterogeneous information network approach. In: Proceedings of the 7th ACM international conference on web search and data mining, pp 283\u2013292","DOI":"10.1145\/2556195.2556259"},{"key":"8940_CR47","doi-asserted-by":"crossref","unstructured":"Perozzi B, Al-Rfou R, Skiena S (2014) DeepWalk: online learning of social representations. In: Proceedings of the 20th ACM SIGKDD international conference on knowledge discovery and data mining, pp 701\u2013710 (2014)","DOI":"10.1145\/2623330.2623732"},{"key":"8940_CR48","doi-asserted-by":"crossref","unstructured":"Grover A, Leskovec J (2016) node2vec: scalable feature learning for networks. In: Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp 855\u2013864","DOI":"10.1145\/2939672.2939754"},{"key":"8940_CR49","doi-asserted-by":"crossref","unstructured":"Dong Y, Chawla NV, Swami A (2017) metapath2vec: scalable representation learning for heterogeneous networks. In: Proceedings of the 23rd ACM SIGKDD international conference on knowledge discovery and data mining, pp 135\u2013144","DOI":"10.1145\/3097983.3098036"},{"key":"8940_CR50","doi-asserted-by":"crossref","unstructured":"Fu T-Y, Lee W-C, Lei Z (2017) Hin2vec: explore meta-paths in heterogeneous information networks for representation learning. In: Proceedings of the 2017 ACM on conference on information and knowledge management, pp 1797\u20131806","DOI":"10.1145\/3132847.3132953"},{"key":"8940_CR51","doi-asserted-by":"crossref","unstructured":"Sun Z, Guo Q, Yang J, Fang H, Guo G, Zhang J, Burke R (2019) Research commentary on recommendations with side information: A survey and research directions. Electronic Commerce Research and Applications 37:100879","DOI":"10.1016\/j.elerap.2019.100879"},{"key":"8940_CR52","doi-asserted-by":"crossref","unstructured":"Gantner Z, Drumond L, Freudenthaler C, Rendle S, Schmidt-Thieme L (2010) Learning attribute-to-feature mappings for cold-start recommendations. In: 2010 IEEE international conference on data mining, pp 176\u2013185","DOI":"10.1109\/ICDM.2010.129"},{"key":"8940_CR53","doi-asserted-by":"crossref","unstructured":"Liang H, Xu Y, Li Y, Nayak R, Tao X (2010) Connecting users and items with weighted tags for personalized item recommendations. In: Proceedings of the 21st ACM conference on hypertext and hypermedia, pp 51\u201360","DOI":"10.1145\/1810617.1810628"},{"key":"8940_CR54","doi-asserted-by":"crossref","unstructured":"Shi Y, Serdyukov P, Hanjalic A, Larson M (2011) Personalized landmark recommendation based on geotags from photo sharing sites. In: Proceedings of the international AAAI conference on web and social media, vol 5, pp 622\u2013625","DOI":"10.1609\/icwsm.v5i1.14152"},{"issue":"1","key":"8940_CR55","first-page":"1","volume":"52","author":"S Zhang","year":"2019","unstructured":"Zhang S, Yao L, Sun A, Tay Y (2019) Deep learning based recommender system: a survey and new perspectives. ACM Comput Surv CSUR 52(1):1\u201338","journal-title":"ACM Comput Surv CSUR"},{"key":"8940_CR56","doi-asserted-by":"crossref","unstructured":"Dong X, Yu L, Wu Z, Sun Y, Yuan L, Zhang F (2017) A hybrid collaborative filtering model with deep structure for recommender systems. In: Proceedings of the AAAI conference on artificial intelligence, vol 31","DOI":"10.1609\/aaai.v31i1.10747"},{"key":"8940_CR57","doi-asserted-by":"crossref","unstructured":"Okura S, Tagami Y, Ono S, Tajima A (2017) Embedding-based news recommendation for millions of users. In: Proceedings of the 23rd ACM SIGKDD international conference on knowledge discovery and data mining, pp 1933\u20131942","DOI":"10.1145\/3097983.3098108"},{"key":"8940_CR58","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 et\u00a0al (2016) Wide & deep learning for recommender systems. In: Proceedings of the 1st workshop on deep learning for recommender systems, pp 7\u201310","DOI":"10.1145\/2988450.2988454"},{"key":"8940_CR59","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, pp 191\u2013198","DOI":"10.1145\/2959100.2959190"},{"key":"8940_CR60","doi-asserted-by":"crossref","unstructured":"Seo S, Huang J, Yang H, Liu Y (2017) Interpretable convolutional neural networks with dual local and global attention for review rating prediction. In: Proceedings of the eleventh ACM conference on recommender systems, pp 297\u2013305","DOI":"10.1145\/3109859.3109890"},{"issue":"4","key":"8940_CR61","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3357154","volume":"37","author":"X Du","year":"2019","unstructured":"Du X, He X, Yuan F, Tang J, Qin Z, Chua T-S (2019) Modeling embedding dimension correlations via convolutional neural collaborative filtering. ACM Trans Inf Syst TOIS 37(4):1\u201322","journal-title":"ACM Trans Inf Syst TOIS"},{"key":"8940_CR62","doi-asserted-by":"crossref","unstructured":"Pei W, Yang J, Sun Z, Zhang J, Bozzon A, Tax DM (2017) Interacting attention-gated recurrent networks for recommendation. In: Proceedings of the 2017 ACM on conference on information and knowledge management, pp 1459\u20131468","DOI":"10.1145\/3132847.3133005"},{"issue":"12","key":"8940_CR63","doi-asserted-by":"publisher","first-page":"2724","DOI":"10.1109\/TKDE.2017.2754499","volume":"29","author":"Q Wang","year":"2017","unstructured":"Wang Q, Mao Z, Wang B, Guo L (2017) Knowledge graph embedding: a survey of approaches and applications. IEEE Trans Knowl Data Eng 29(12):2724\u20132743","journal-title":"IEEE Trans Knowl Data Eng"},{"issue":"9","key":"8940_CR64","doi-asserted-by":"publisher","first-page":"1616","DOI":"10.1109\/TKDE.2018.2807452","volume":"30","author":"H Cai","year":"2018","unstructured":"Cai H, Zheng VW, Chang KC-C (2018) A comprehensive survey of graph embedding: Problems, techniques, and applications. IEEE Trans Knowl Data Eng 30(9):1616\u20131637","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"8940_CR65","doi-asserted-by":"crossref","unstructured":"Wang X, Wang D, Xu C, He X, Cao Y, Chua T-S (2019) Explainable reasoning over knowledge graphs for recommendation. In: Proceedings of the AAAI conference on artificial intelligence, vol 33, pp 5329\u20135336","DOI":"10.1609\/aaai.v33i01.33015329"},{"key":"8940_CR66","doi-asserted-by":"crossref","unstructured":"Wang H, Zhao M, Xie X, Li W, Guo M (2019) Knowledge graph convolutional networks for recommender systems. In: The world wide web conference, pp 3307\u20133313","DOI":"10.1145\/3308558.3313417"},{"key":"8940_CR67","doi-asserted-by":"crossref","unstructured":"Wang X, He X, Cao Y, Liu M, Chua T-S (2019) KGAT: knowledge graph attention network for recommendation. In: Proceedings of the 25th ACM SIGKDD international conference on knowledge discovery & data mining, pp 950\u2013958","DOI":"10.1145\/3292500.3330989"},{"key":"8940_CR68","doi-asserted-by":"crossref","unstructured":"Lin Y, Liu Z, Sun M, Liu Y, Zhu X (2015) Learning entity and relation embeddings for knowledge graph completion. In: Twenty-ninth AAAI conference on artificial intelligence","DOI":"10.1609\/aaai.v29i1.9491"},{"key":"8940_CR69","doi-asserted-by":"crossref","unstructured":"Chen L, Wu L, Hong R, Zhang K, Wang M (2020) Revisiting graph based collaborative filtering: a linear residual graph convolutional network approach. In: Proceedings of the AAAI conference on artificial intelligence, vol 34, pp 27\u201334","DOI":"10.1609\/aaai.v34i01.5330"},{"key":"8940_CR70","unstructured":"Cai C, Wang Y (2020) A note on over-smoothing for graph neural networks. Computing Research Repository (CoRR)"},{"key":"8940_CR71","doi-asserted-by":"crossref","unstructured":"Li Q, Han Z, Wu X-M (2018) Deeper insights into graph convolutional networks for semi-supervised learning. In: Thirty-second AAAI conference on artificial intelligence","DOI":"10.1609\/aaai.v32i1.11604"},{"key":"8940_CR72","unstructured":"Huang W, Rong Y, Xu T, Sun F, Huang J (2020) Tackling over-smoothing for general graph convolutional networks. Computing Research Repository (CoRR)"},{"key":"8940_CR73","doi-asserted-by":"crossref","unstructured":"Yan Y, Hashemi M, Swersky K, Yang Y, Koutra D (2021) Two sides of the same coin: Heterophily and oversmoothing in graph convolutional neural networks. Computing Research Repository (CoRR)","DOI":"10.1109\/ICDM54844.2022.00169"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-023-08940-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-023-08940-z\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-023-08940-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,9,16]],"date-time":"2023-09-16T15:12:50Z","timestamp":1694877170000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-023-08940-z"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,8,17]]},"references-count":73,"journal-issue":{"issue":"30","published-print":{"date-parts":[[2023,10]]}},"alternative-id":["8940"],"URL":"https:\/\/doi.org\/10.1007\/s00521-023-08940-z","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,8,17]]},"assertion":[{"value":"4 May 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"1 August 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"17 August 2023","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no competing interests that are relevant to the content of this article.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}