About author:LI Lei, born in 1951, Ph. D., professor. His research interests include artificial intelligence, database, machine learning. ZHANG Jiaqiang, born in 1999. His research interests include recommendation system, data mining.
Supported by:
the Special Fund for Scientific and Technological Innovation Cultivation of College Students in Guangdong Province in 2020(pdjh2020b1363);the “Innovation and Strengthening University” Special Innovation Project of the Department of Education of Guangdong Province in 2020(2020KTSCX367);the Guangdong University Innovation Team Project in 2020 (Natural Science)(2020KCXTD051)
Gai LI, Lei LI, Jiaqiang ZHANG. Social collaborative ranking recommendation algorithm by exploiting both explicit and implicit feedback[J]. Journal of Computer Applications, 2021, 41(12): 3515-3520.
LI G, XU Q Z, LI L, et al. TLRank: a new social collaborative ranking recommendation algorithm[J]. Journal of South China Normal University (Natural Science Edition), 2019, 51(5): 121-128.
LI G, CHEN Q, LI L. Collaborative filtering recommendation algorithm based on rating prediction and ranking prediction[J]. Acta Electronica Sinica, 2017, 45(12): 3070-3075. 10.3969/j.issn.0372-2112.2017.12.033
LI G. Collaborative ranking algorithm by explicit and implicit feedback fusion[J]. Journal of Computer Applications, 2015, 35(5): 1328-1332, 1341. 10.11772/j.issn.1001-9081.2015.05.1328
HUANG Z H, ZHANG J W, TIAN C Q, et al. Survey on learning-to-rank based recommendation algorithms[J]. Journal of Software, 2016, 27(3): 691-713. 10.13328/j.cnki.jos.004948
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YAO W L, HE J, HUANG G Y, et al. SoRank: incorporating social information into learning to rank models for recommendation[C]// Proceedings of the 23rd International Conference on World Wide Web. New York: ACM, 2014: 409-410. 10.1145/2567948.2577333
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HUANG C, XU H C, XU Y, et al. Knowledge-aware coupled graph neural network for social recommendation[C]// Proceedings of the 35th AAAI International Conference on Artificial Intelligence. Palo Alto, CA: AAAI Press, 2021: 4115-4122. 10.1609/aaai.v33i01.33015573
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LIU C H, WANG X, LU T, et al. Discrete social recommendation[C]// Proceedings of the 33rd AAAI Conference on Artificial Intelligence. Palo Alto, CA: AAAI Press, 2019: 208-215. 10.1609/aaai.v33i01.3301208
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DU L, LI X, SHEN Y D. User graph regularized pairwise matrix factorization for item recommendation[C]// Proceedings of the 7th International Conference on Advanced Data Mining and Applications, LNCS7121. Berlin: Springer, 2011: 372-385.
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KROHN-GRIMBERGHE A, DRUMOND L, FREUDENTHALER C, et al. Multi-relational matrix factorization using Bayesian personalized ranking for social network data[C]// Proceedings of the 5th ACM International Conference on Web Search and Data Mining. New York: ACM, 2012: 173-182. 10.1145/2124295.2124317
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ZHAO T, McAULEY J, KING I. Leveraging social connections to improve personalized ranking for collaborative filtering[C]// Proceedings of the 23rd ACM International Conference on Information and Knowledge Management. New York: ACM, 2014: 261-270. 10.1145/2661829.2661998
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GUO L, MA J, JIANG H R, et al. Social trust aware item recommendation for implicit feedback[J]. Journal of Computer Science and Technology, 2015, 30(5): 1039-1053. 10.1007/s11390-015-1580-8
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GUO G B, ZHANG J, YORKE-SMITH N. A novel recommendation model regularized with user trust and item ratings[J]. IEEE Transactions on Knowledge and Data Engineering, 2016, 28(7): 1607-1620. 10.1109/tkde.2016.2528249
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SHI Y, KARATZOGLOU A, BALTRUNAS L, et al. xCLiMF: optimizing expected reciprocal rank for data with multiple levels of relevance[C]// Proceedings of the 7th ACM Conference on Recommender Systems. New York: ACM, 2013: 431-434. 10.1145/2507157.2507227
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KOREN Y. Factor in the neighbors: scalable and accurate collaborative filtering[J]. ACM Transactions on Knowledge Discovery from Data, 2010, 4(1): No.1. 10.1145/1644873.1644874
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YANG B, LEI Y, LIU D Y, et al. Social collaborative filtering by trust[C]// Proceedings of the 23rd International Joint Conference on Artificial Intelligence. Palo Alto, CA: AAAI Press, 2013: 2747-2753. 10.2172/1749853