{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,2]],"date-time":"2026-01-02T07:11:04Z","timestamp":1767337864221,"version":"3.40.3"},"publisher-location":"Singapore","reference-count":50,"publisher":"Springer Nature Singapore","isbn-type":[{"type":"print","value":"9789819932993"},{"type":"electronic","value":"9789819933006"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"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":[],"published-print":{"date-parts":[[2023]]},"DOI":"10.1007\/978-981-99-3300-6_12","type":"book-chapter","created":{"date-parts":[[2023,5,30]],"date-time":"2023-05-30T15:04:01Z","timestamp":1685459041000},"page":"155-171","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["A Survey of the State-of-the-Art and Some Extensions of Recommender System Based on Big Data"],"prefix":"10.1007","author":[{"given":"Lixin","family":"Jia","sequence":"first","affiliation":[]},{"given":"Lixiu","family":"Jia","sequence":"additional","affiliation":[]},{"given":"Lihang","family":"Feng","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,5,31]]},"reference":[{"doi-asserted-by":"crossref","unstructured":"Hdioud, F., Frikh, B., Ouhbi, B.: A comparison study of some algorithms in Recommender Systems. In: 2012 Colloquium in Information Science and Technology, pp. 130\u2013135 (2012)","key":"12_CR1","DOI":"10.1109\/CIST.2012.6388076"},{"issue":"3","key":"12_CR2","doi-asserted-by":"publisher","first-page":"410","DOI":"10.1016\/j.intcom.2005.11.004","volume":"18","author":"G Lekakos","year":"2006","unstructured":"Lekakos, G., Giaglis, G.M.: Improving the prediction accuracy of recommendation algorithms: approaches anchored on human factors. Interact. Comput. 18(3), 410\u2013431 (2006)","journal-title":"Interact. Comput."},{"issue":"6","key":"12_CR3","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.: 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 (2005)","journal-title":"IEEE Trans. Knowl. Data Eng."},{"doi-asserted-by":"crossref","unstructured":"Ghazanfar, M.A., et al.: A scalable, accurate hybrid recommender system. In: 2010 Third International Conference on Knowledge Discovery and Data Mining, pp. 94\u201398 (2010)","key":"12_CR4","DOI":"10.1109\/WKDD.2010.117"},{"issue":"8","key":"12_CR5","doi-asserted-by":"publisher","first-page":"1478","DOI":"10.1109\/TKDE.2011.90","volume":"24","author":"M Hornick","year":"2012","unstructured":"Hornick, M., Tamayo, P.: Extending recommender systems for disjoint user\/item sets: the conference recommendation problem. IEEE Trans. Knowl. Data Eng. 24(8), 1478\u20131490 (2012)","journal-title":"IEEE Trans. Knowl. Data Eng."},{"issue":"3","key":"12_CR6","doi-asserted-by":"publisher","first-page":"81","DOI":"10.1109\/MPRV.2010.56","volume":"9","author":"M Munoz-Organero","year":"2010","unstructured":"Munoz-Organero, M., et al.: A collaborative recommender system based on space-time similarities. IEEE Pervasive Comput. 9(3), 81\u201387 (2010)","journal-title":"IEEE Pervasive Comput."},{"key":"12_CR7","doi-asserted-by":"publisher","first-page":"104673","DOI":"10.1109\/ACCESS.2019.2931659","volume":"7","author":"F Liang","year":"2019","unstructured":"Liang, F., et al.: Search engine for the internet of things: lessons from web search, vision, and opportunities. IEEE Access 7, 104673\u2013104691 (2019)","journal-title":"IEEE Access"},{"issue":"4","key":"12_CR8","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/22339.22340","volume":"17","author":"TW Malone","year":"1986","unstructured":"Malone, T.W., et al.: The information lens: an intelligent system for information sharing in organizations. Acm Sigchi Bull. 17(4), 1\u20138 (1986)","journal-title":"Acm Sigchi Bull."},{"doi-asserted-by":"crossref","unstructured":"Balabanovi\u0107, M.: An adaptive Web page recommendation service. In: Proceedings of the first International Conference on Autonomous Agents, pp. 378\u2013385 (1997)","key":"12_CR9","DOI":"10.1145\/267658.267744"},{"doi-asserted-by":"crossref","unstructured":"Armstrong, R., et al.: Webwatcher: a learning apprentice for the world wide web. In: AAAI Spring Symposium on Information Gathering from Heterogeneous, Distributed Environments, vol. 93, p. 107. Stanford (1995)","key":"12_CR10","DOI":"10.21236\/ADA640219"},{"issue":"12","key":"12_CR11","doi-asserted-by":"publisher","first-page":"61","DOI":"10.1145\/138859.138867","volume":"35","author":"D Goldberg","year":"1992","unstructured":"Goldberg, D., et al.: Using collaborative filtering to weave an information tapestry. Commun. ACM 35(12), 61\u201370 (1992)","journal-title":"Commun. ACM"},{"doi-asserted-by":"crossref","unstructured":"Maltz, D., Ehrlich, K.: Pointing the way: active collaborative filtering. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (1995)","key":"12_CR12","DOI":"10.1145\/223904.223930"},{"issue":"4","key":"12_CR13","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/2827872","volume":"5","author":"FM Harper","year":"2015","unstructured":"Harper, F.M., Konstan, J.A.: The movielens datasets: history and context. ACM Trans. Interact. Intell. Syst. 5(4), 1\u201319 (2015)","journal-title":"ACM Trans. Interact. Intell. Syst."},{"key":"12_CR14","doi-asserted-by":"publisher","first-page":"14112","DOI":"10.1109\/ACCESS.2019.2960523","volume":"8","author":"J Jiao","year":"2020","unstructured":"Jiao, J., et al.: A novel learning rate function and its application on the SVD++ recommendation algorithm. IEEE Access 8, 14112\u201314122 (2020)","journal-title":"IEEE Access"},{"unstructured":"Chen, T., et al.: Xgboost: extreme gradient boosting. R package version 0.4\u20132, pp. 1\u20134 (2015)","key":"12_CR15"},{"unstructured":"Ke, G., et al.: Lightgbm: a highly efficient gradient boosting decision tree. Advances in Neural Information Processing Systems 30 (2017)","key":"12_CR16"},{"doi-asserted-by":"crossref","unstructured":"Lemire, D., Maclachlan, A.: Slope one predictors for online ratingbased collaborative filtering. In Proceedings of the 2005 SIAM International Conference on Data Mining, pp. 471\u2013480 (2005)","key":"12_CR17","DOI":"10.1137\/1.9781611972757.43"},{"unstructured":"Huang, M.: Design and Implementation of Incremental Music Recommendation System Based on Slope One Algorithm. Chongqing University (2016)","key":"12_CR18"},{"unstructured":"Do, M.P.T., et al.: Model-based approach for collaborative filtering. In: 6th International Conference on Information Technology for Education, pp. 217\u2013228 (2010)","key":"12_CR19"},{"issue":"2","key":"12_CR20","doi-asserted-by":"publisher","first-page":"1273","DOI":"10.1109\/TII.2014.2308433","volume":"10","author":"X Luo","year":"2014","unstructured":"Luo, X., et al.: An efficient non-negative matrix-factorization-based approach to collaborative filtering for recommender systems. IEEE Trans. Industr. Inf. 10(2), 1273\u20131284 (2014)","journal-title":"IEEE Trans. Industr. Inf."},{"doi-asserted-by":"crossref","unstructured":"Wang, H.: MatMat: matrix factorization by matrix fitting. In: 2021 IEEE 4th International Conference on Information Systems and Computer Aided Education (ICISCAE), pp.99\u2013101 (2021)","key":"12_CR21","DOI":"10.1109\/ICISCAE52414.2021.9590639"},{"doi-asserted-by":"crossref","unstructured":"Wang, H.: MovieMat: context-aware movie recommendation with matrix factorization by matrix fitting. In: 2021 7th International Conference on Computer and Communications (ICCC), pp. 1642\u20131645 (2021)","key":"12_CR22","DOI":"10.1109\/ICCC54389.2021.9674549"},{"doi-asserted-by":"crossref","unstructured":"Jamali, M., Lakshmanan, L.: 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 (2013)","key":"12_CR23","DOI":"10.1145\/2488388.2488445"},{"doi-asserted-by":"crossref","unstructured":"Shi, C., et al.: Semantic path based personalized recommendation on weighted heterogeneous information networks. In: Acm International on Conference on Information and Knowledge Management, pp. 453\u2013462 (2015)","key":"12_CR24","DOI":"10.1145\/2806416.2806528"},{"issue":"2","key":"12_CR25","doi-asserted-by":"publisher","first-page":"20","DOI":"10.1145\/2481244.2481248","volume":"14","author":"Y Sun","year":"2013","unstructured":"Sun, Y., Han, J.: Mining heterogeneous information networks: a structural analysis approach. ACM SIGKDD Explor. Newsl. 14(2), 20\u201328 (2013)","journal-title":"ACM SIGKDD Explor. Newsl."},{"doi-asserted-by":"crossref","unstructured":"Xu, J., et al.: Local matrix factorization with social network embedding. In: 2021 IEEE International Conference on Artificial Intelligence and Industrial Design (AIID), pp. 492\u2013495 (2021)","key":"12_CR26","DOI":"10.1109\/AIID51893.2021.9456514"},{"doi-asserted-by":"crossref","unstructured":"Barathy, R., et al.: Applying matrix factorization in collaborative filtering recommender systems. In: 2020 6th international conference on advanced computing and communication systems (ICACCS), pp. 635\u2013639 (2022)","key":"12_CR27","DOI":"10.1109\/ICACCS48705.2020.9074227"},{"doi-asserted-by":"crossref","unstructured":"Ma, H., et al.: Sorec: social recommendation using probabilistic matrix factorization. In: Proceedings of the 17th ACM Conference on Information and Knowledge Management, pp. 931\u2013940 (2008)","key":"12_CR28","DOI":"10.1145\/1458082.1458205"},{"issue":"8","key":"12_CR29","first-page":"1663","volume":"39","author":"B Yang","year":"2013","unstructured":"Yang, B., et al.: Social collaborative filtering by trust. IEEE Trans. Pattern Anal. Mach. Intell. 39(8), 1663\u20131747 (2013)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"doi-asserted-by":"crossref","unstructured":"Han, J., Pan, Y.: A noise-aware asymmetric spectral regularization collective matrix factorization algorithm for recommender system in cloud services. In: 2020 IEEE 13th International Conference on Cloud Computing (CLOUD), pp.502\u2013506 (2020)","key":"12_CR30","DOI":"10.1109\/CLOUD49709.2020.00074"},{"doi-asserted-by":"crossref","unstructured":"Laseno, F.U.D., et al.: Knowledge-based filtering recommender system to propose design elements of serious game. In: 2019 International Conference on Electrical Engineering and Informatics (ICEEI), pp.158\u2013163 (2019)","key":"12_CR31","DOI":"10.1109\/ICEEI47359.2019.8988797"},{"issue":"2","key":"12_CR32","first-page":"17","volume":"2","author":"T Tran","year":"2007","unstructured":"Tran, T.: Combining collaborative filtering and knowledge-based approaches for better recommendation systems. J. Bus. Technol. 2(2), 17\u201324 (2007)","journal-title":"J. Bus. Technol."},{"issue":"6","key":"12_CR33","first-page":"23","volume":"40","author":"FY Zhou","year":"2017","unstructured":"Zhou, F.Y., Jin, L.P., Dong, J.: Review of convolutional neural network. Chin. J. Comput. 40(6), 23 (2017)","journal-title":"Chin. J. Comput."},{"doi-asserted-by":"crossref","unstructured":"Yan, C., Shi, Y.: A personalized location recommendation based on convolutional neural network. In: 2020 IEEE 5th Information Technology and Mechatronics Engineering Conference (ITOEC), pp. 1516\u20131519 (2020)","key":"12_CR34","DOI":"10.1109\/ITOEC49072.2020.9141833"},{"unstructured":"Liu, J.: Research on Application of Autoencoder in Recommendation System. Tianjin University of Technology (2022)","key":"12_CR35"},{"unstructured":"Chen, R.: Recommendation Algorithm Based on Heterogeneous Graph Attention Network and Recurrent Neural Network. Chongqing University (2021)","key":"12_CR36"},{"doi-asserted-by":"crossref","unstructured":"Yao, Q., Liao, X., Jin, H.: Hierarchical attention based recurrent neural network framework for mobile MOBA game recommender systems. In: 2018 IEEE Intl Conf on Parallel & Distributed Processing with Applications, Ubiquitous Computing & Communications, Big Data & Cloud Computing, Social Computing & Networking, Sustainable Computing & Communications (ISPA\/IUCC\/BDCloud\/SocialCom\/SustainCom), pp. 338\u2013345. (2018)","key":"12_CR37","DOI":"10.1109\/BDCloud.2018.00060"},{"unstructured":"Ruimeng, C.: Research and Application of Recommendation Algorithm Based on Recurrent Neural Network and Weighted Knowledge Graph. Chongqing University of Technology (2021)","key":"12_CR38"},{"unstructured":"Xu, B.: Research on Fairness of Recommendation System Based on Knowledge Graph. School of Computer Science and Engineering (2022)","key":"12_CR39"},{"doi-asserted-by":"crossref","unstructured":"Yin, H., et al.: Social influence-based group representation learning for group recommendation. In: 2019 IEEE 35th International Conference on Data Engineering (ICDE), pp. 566\u2013577 (2019)","key":"12_CR40","DOI":"10.1109\/ICDE.2019.00057"},{"doi-asserted-by":"crossref","unstructured":"Guo, L., et al.: Group recommendation with latent voting mechanism. In: 2020 IEEE 36th International Conference on Data Engineering (ICDE), pp. 121\u2013132 (2020)","key":"12_CR41","DOI":"10.1109\/ICDE48307.2020.00018"},{"unstructured":"Zhiyi, D.: Research on the Solution of Data Sparsity Problem of Recommendation System Based on Knowledge Graph. School of Computer Science and Engineering (2022)","key":"12_CR42"},{"unstructured":"Yinren, L.: Enhanced Personalized Learning Recommender System Based on Knowledge Graph. School of Computer Science and Engineering (2022)","key":"12_CR43"},{"doi-asserted-by":"crossref","unstructured":"G\u00fcrb\u00fcz, H.G., Tekinerdogan, B.: Software metrics for green parallel computing of big data systems. In: 2016 IEEE International Congress on Big Data (BigData Congress), pp. 345\u2013348 (2016)","key":"12_CR44","DOI":"10.1109\/BigDataCongress.2016.54"},{"issue":"7","key":"12_CR45","doi-asserted-by":"publisher","first-page":"1530","DOI":"10.1109\/TPDS.2017.2718515","volume":"29","author":"H Li","year":"2017","unstructured":"Li, H., et al.: MSGD: a novel matrix factorization approach for large-scale collaborative filtering recommender systems on GPUs. IEEE Trans. Parallel Distrib. Syst. 29(7), 1530\u20131544 (2017)","journal-title":"IEEE Trans. Parallel Distrib. Syst."},{"doi-asserted-by":"crossref","unstructured":"Zhao, X.: A study on e-commerce recommender system based on big data. In: 2019 IEEE 4th International Conference on Cloud Computing and Big Data Analysis (ICCCBDA), pp. 222\u2013226 (2019)","key":"12_CR46","DOI":"10.1109\/ICCCBDA.2019.8725694"},{"doi-asserted-by":"crossref","unstructured":"Koohi, A., Homayoun, H.: Parallel multi-view graph matrix completion for large input matrix. In: 2019 IEEE 9th Annual Computing and Communication Workshop and Conference (CCWC), pp. 0337\u20130341 (2019)","key":"12_CR47","DOI":"10.1109\/CCWC.2019.8666532"},{"issue":"1","key":"12_CR48","doi-asserted-by":"publisher","first-page":"91","DOI":"10.1109\/TNSE.2018.2862948","volume":"7","author":"J Sun","year":"2020","unstructured":"Sun, J., et al.: A parallel recommender system using a collaborative filtering algorithm with correntropy for social networks. IEEE Trans. Netw. Sci. Eng. 7(1), 91\u2013103 (2020)","journal-title":"IEEE Trans. Netw. Sci. Eng."},{"doi-asserted-by":"crossref","unstructured":"Al-Doulat, A.: Surprise and curiosity in a recommender system. In: 2018 IEEE\/ACS 15th International Conference on Computer Systems and Applications (AICCSA), pp. 1\u20132 (2018)","key":"12_CR49","DOI":"10.1109\/AICCSA.2018.8612897"},{"doi-asserted-by":"crossref","unstructured":"Abbas, F.: Serendipity in recommender system: a holistic overview. In: 2018 IEEE\/ACS 15th International Conference on Computer Systems and Applications (AICCSA), pp. 1\u20132 (2018)","key":"12_CR50","DOI":"10.1109\/AICCSA.2018.8612895"}],"container-title":["Communications in Computer and Information Science","Big Data and Security"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-99-3300-6_12","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,5,30]],"date-time":"2023-05-30T15:06:19Z","timestamp":1685459179000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-99-3300-6_12"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9789819932993","9789819933006"],"references-count":50,"URL":"https:\/\/doi.org\/10.1007\/978-981-99-3300-6_12","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"type":"print","value":"1865-0929"},{"type":"electronic","value":"1865-0937"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"31 May 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICBDS","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Big Data and Security","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Xiamen","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8 December 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"12 December 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"4","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icbds2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.icbds.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}