{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,16]],"date-time":"2025-10-16T06:59:15Z","timestamp":1760597955272,"version":"3.41.0"},"reference-count":44,"publisher":"Association for Computing Machinery (ACM)","issue":"4","license":[{"start":{"date-parts":[[2020,5,25]],"date-time":"2020-05-25T00:00:00Z","timestamp":1590364800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["61872249, 61836005, and 61672358"],"award-info":[{"award-number":["61872249, 61836005, and 61672358"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Intell. Syst. Technol."],"published-print":{"date-parts":[[2020,8,31]]},"abstract":"<jats:p>With the explosive growth of web resources, an increasingly important task in recommender systems is to provide high-quality personalized services by learning users\u2019 preferences from historically observed information. As an effective preference learning technology, collaborative filtering has been widely extended to model the one-class or implicit feedback data, which is known as one-class collaborative filtering (OCCF). For a long time, pairwise ranking-oriented learning scheme has been viewed as a superior solution than the pointwise scheme for OCCF due to its higher accuracy in most cases. However, we argue that with appropriate model design, pointwise preference learning can achieve comparable or even better performance than the counterpart, i.e., pairwise preference learning. In particular, we propose a new preference assumption, i.e., pointwise preference on user\/item-set. Based on this new assumption, we develop a novel, simple, and flexible solution called collaborative filtering via pointwise preference learning on user\/item-set (CoFi-points). Furthermore, we derive two specific algorithms of CoFi-points with respect to the involved user-set and item-set, i.e., CoFi-points(u) and CoFi-points(i), referring to preference assumptions defined on user-set and item-set, respectively. Finally, we conduct extensive empirical studies on four real-world datasets with the state-of-the-art methods, and find that our solution can achieve very promising performance with respect to several ranking-oriented evaluation metrics.<\/jats:p>","DOI":"10.1145\/3389127","type":"journal-article","created":{"date-parts":[[2020,5,26]],"date-time":"2020-05-26T00:05:14Z","timestamp":1590451514000},"page":"1-24","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":4,"title":["CoFi-points"],"prefix":"10.1145","volume":"11","author":[{"given":"Lin","family":"Li","sequence":"first","affiliation":[{"name":"College of Computer Science and Software Engineering and National Engineering Laboratory for Big Data System Computing Technology, Shenzhen University, Shenzhen"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6326-9531","authenticated-orcid":false,"given":"Weike","family":"Pan","sequence":"additional","affiliation":[{"name":"College of Computer Science and Software Engineering and National Engineering Laboratory for Big Data System Computing Technology, Shenzhen University, Shenzhen"}]},{"given":"Zhong","family":"Ming","sequence":"additional","affiliation":[{"name":"College of Computer Science and Software Engineering and National Engineering Laboratory for Big Data System Computing Technology, Shenzhen University, Shenzhen"}]}],"member":"320","published-online":{"date-parts":[[2020,5,25]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2005.99"},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2016.2566622"},{"key":"e_1_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1145\/3017429"},{"key":"e_1_2_1_4_1","volume-title":"Lyu","author":"Cheng Chen","year":"2016","unstructured":"Chen Cheng , Haiqin Yang , Irwin King , and Michael R . Lyu . 2016 . A unified point-of-interest recommendation framework in location-based social networks. ACM TIST|Intell. Syst. Technol . 8, 1 (2016), 10:1--10:21. Chen Cheng, Haiqin Yang, Irwin King, and Michael R. Lyu. 2016. A unified point-of-interest recommendation framework in location-based social networks. ACM TIST|Intell. Syst. Technol. 8, 1 (2016), 10:1--10:21."},{"key":"e_1_2_1_5_1","volume-title":"Proceedings of the AAAI Conference on Artificial Intelligence","volume":"33","author":"Deng Zhi-Hong","unstructured":"Zhi-Hong Deng , Ling Huang , Chang-Dong Wang , Jian-Huang Lai , and S. Yu Philip . 2019. Deepcf: A unified framework of representation learning and matching function learning in recommender system . In Proceedings of the AAAI Conference on Artificial Intelligence , Vol. 33 . 61--68. Zhi-Hong Deng, Ling Huang, Chang-Dong Wang, Jian-Huang Lai, and S. Yu Philip. 2019. Deepcf: A unified framework of representation learning and matching function learning in recommender system. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 33. 61--68."},{"key":"e_1_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1145\/3132847.3132941"},{"key":"e_1_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1145\/138859.138867"},{"key":"e_1_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1145\/2939672.2939754"},{"key":"e_1_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1145\/3038912.3052569"},{"key":"e_1_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1145\/3269206.3271785"},{"key":"e_1_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM.2008.22"},{"key":"e_1_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.5555\/1304060.1304521"},{"key":"e_1_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2015.2432811"},{"key":"e_1_2_1_14_1","volume-title":"Proceedings of the Workshop on Distributed Machine Learning and Matrix Computations (NeurIPS\u201914)","author":"Johnson Christopher C.","year":"2014","unstructured":"Christopher C. Johnson . 2014 . Logistic matrix factorization for implicit feedback data . In Proceedings of the Workshop on Distributed Machine Learning and Matrix Computations (NeurIPS\u201914) . Christopher C. Johnson. 2014. Logistic matrix factorization for implicit feedback data. In Proceedings of the Workshop on Distributed Machine Learning and Matrix Computations (NeurIPS\u201914)."},{"key":"e_1_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1145\/2487575.2487589"},{"key":"e_1_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.14778\/2336664.2336669"},{"key":"e_1_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.1145\/1401890.1401944"},{"key":"e_1_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.1145\/2124295.2124317"},{"key":"e_1_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.1145\/3132847.3133100"},{"key":"e_1_2_1_20_1","volume-title":"Proceedings of the Workshop on Multi-dimensional Information Fusion for User Modeling and Personalization.","author":"Li Lin","year":"2018","unstructured":"Lin Li , Weike Pan , and Zhong Ming . 2018 . CoFi-points: Collaborative filtering via pointwise preference learning over item-sets . In Proceedings of the Workshop on Multi-dimensional Information Fusion for User Modeling and Personalization. Retrieved from https:\/\/www.librec.net\/ifup\/2018\/. Lin Li, Weike Pan, and Zhong Ming. 2018. CoFi-points: Collaborative filtering via pointwise preference learning over item-sets. In Proceedings of the Workshop on Multi-dimensional Information Fusion for User Modeling and Personalization. Retrieved from https:\/\/www.librec.net\/ifup\/2018\/."},{"key":"e_1_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.1145\/3182166"},{"key":"e_1_2_1_22_1","volume-title":"Proceedings of the 32nd AAAI Conference on Artificial Intelligence (AAAI\u201918)","author":"Liu Bo","year":"2018","unstructured":"Bo Liu , Ying Wei , Yu Zhang , Zhixian Yan , and Qiang Yang . 2018 . Transferable contextual bandit for cross-domain recommendation . In Proceedings of the 32nd AAAI Conference on Artificial Intelligence (AAAI\u201918) . 3619--3626. Bo Liu, Ying Wei, Yu Zhang, Zhixian Yan, and Qiang Yang. 2018. Transferable contextual bandit for cross-domain recommendation. In Proceedings of the 32nd AAAI Conference on Artificial Intelligence (AAAI\u201918). 3619--3626."},{"key":"e_1_2_1_23_1","volume-title":"Proceedings of the 32nd AAAI Conference on Artificial Intelligence (AAAI\u201918)","author":"Meng Xuying","year":"2018","unstructured":"Xuying Meng , Suhang Wang , Kai Shu , Jundong Li , Bo Chen , Huan Liu , and Yujun Zhang . 2018 . Personalized privacy-preserving social recommendation . In Proceedings of the 32nd AAAI Conference on Artificial Intelligence (AAAI\u201918) . 3796--3803. Xuying Meng, Suhang Wang, Kai Shu, Jundong Li, Bo Chen, Huan Liu, and Yujun Zhang. 2018. Personalized privacy-preserving social recommendation. In Proceedings of the 32nd AAAI Conference on Artificial Intelligence (AAAI\u201918). 3796--3803."},{"key":"e_1_2_1_24_1","unstructured":"Tomas Mikolov Kai Chen Greg S. Corrado and Jeffrey Dean. 2013a. Efficient Estimation of Word Representations in Vector Space. Retrieved from http:\/\/arxiv.org\/abs\/1301.3781  Tomas Mikolov Kai Chen Greg S. Corrado and Jeffrey Dean. 2013a. Efficient Estimation of Word Representations in Vector Space. Retrieved from http:\/\/arxiv.org\/abs\/1301.3781"},{"key":"e_1_2_1_25_1","volume-title":"Proceedings of the 26th International Conference on Neural Information Processing Systems (NeurIPS\u201913)","author":"Mikolov Tomas","year":"2013","unstructured":"Tomas Mikolov , Ilya Sutskever , Kai Chen , Greg Corrado , and Jeffrey Dean . 2013 b. Distributed representations of words and phrases and their compositionality . In Proceedings of the 26th International Conference on Neural Information Processing Systems (NeurIPS\u201913) . 3111--3119. Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg Corrado, and Jeffrey Dean. 2013b. Distributed representations of words and phrases and their compositionality. In Proceedings of the 26th International Conference on Neural Information Processing Systems (NeurIPS\u201913). 3111--3119."},{"key":"e_1_2_1_26_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM.2008.16"},{"key":"e_1_2_1_27_1","doi-asserted-by":"publisher","DOI":"10.1137\/1.9781611972832.20"},{"key":"e_1_2_1_28_1","volume-title":"Proceedings of the 23rd International Joint Conference on Artificial Intelligence (IJCAI\u201913)","author":"Pan Weike","year":"2013","unstructured":"Weike Pan and Li Chen . 2013 b. GBPR: Group preference based Bayesian personalized ranking for one-class collaborative filtering . In Proceedings of the 23rd International Joint Conference on Artificial Intelligence (IJCAI\u201913) . 2691--2697. Weike Pan and Li Chen. 2013b. GBPR: Group preference based Bayesian personalized ranking for one-class collaborative filtering. In Proceedings of the 23rd International Joint Conference on Artificial Intelligence (IJCAI\u201913). 2691--2697."},{"key":"e_1_2_1_29_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2016.05.019"},{"key":"e_1_2_1_30_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10115-018-1154-5"},{"key":"e_1_2_1_31_1","doi-asserted-by":"publisher","DOI":"10.1145\/2168752.2168771"},{"key":"e_1_2_1_32_1","volume-title":"Proceedings of the 25th Conference on Uncertainty in Artificial Intelligence (UAI\u201909)","author":"Rendle Steffen","year":"2009","unstructured":"Steffen Rendle , Christoph Freudenthaler , Zeno Gantner , and Lars Schmidt-Thieme . 2009 . BPR: Bayesian personalized ranking from implicit feedback . In Proceedings of the 25th Conference on Uncertainty in Artificial Intelligence (UAI\u201909) . 452--461. Steffen Rendle, Christoph Freudenthaler, Zeno Gantner, and Lars Schmidt-Thieme. 2009. BPR: Bayesian personalized ranking from implicit feedback. In Proceedings of the 25th Conference on Uncertainty in Artificial Intelligence (UAI\u201909). 452--461."},{"key":"e_1_2_1_33_1","volume-title":"Proceedings of the Annual Conference on Neural Information Processing Systems (NeurIPS\u201908)","author":"Salakhutdinov Ruslan","year":"2008","unstructured":"Ruslan Salakhutdinov and Andriy Mnih . 2008 . Probabilistic matrix factorization . In Proceedings of the Annual Conference on Neural Information Processing Systems (NeurIPS\u201908) . 1257--1264. Ruslan Salakhutdinov and Andriy Mnih. 2008. Probabilistic matrix factorization. In Proceedings of the Annual Conference on Neural Information Processing Systems (NeurIPS\u201908). 1257--1264."},{"key":"e_1_2_1_34_1","doi-asserted-by":"publisher","DOI":"10.1145\/2365952.2365981"},{"key":"e_1_2_1_35_1","doi-asserted-by":"publisher","DOI":"10.1145\/2556270"},{"key":"e_1_2_1_36_1","doi-asserted-by":"publisher","DOI":"10.1145\/3269206.3271715"},{"key":"e_1_2_1_37_1","doi-asserted-by":"publisher","DOI":"10.1145\/2736277.2741093"},{"key":"e_1_2_1_38_1","doi-asserted-by":"publisher","DOI":"10.1145\/3331184.3331267"},{"key":"e_1_2_1_39_1","volume-title":"Proceedings of the 35th International Conference on Machine Learning. 5311--5320","author":"Wu Liwei","year":"2018","unstructured":"Liwei Wu , Cho-Jui Hsieh , and James Sharpnack . 2018 . SQL-Rank: A listwise approach to collaborative ranking . In Proceedings of the 35th International Conference on Machine Learning. 5311--5320 . Liwei Wu, Cho-Jui Hsieh, and James Sharpnack. 2018. SQL-Rank: A listwise approach to collaborative ranking. In Proceedings of the 35th International Conference on Machine Learning. 5311--5320."},{"key":"e_1_2_1_40_1","doi-asserted-by":"publisher","DOI":"10.1145\/2835776.2835837"},{"key":"e_1_2_1_41_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-39937-9_19"},{"key":"e_1_2_1_42_1","doi-asserted-by":"publisher","DOI":"10.1145\/3269206.3269283"},{"key":"e_1_2_1_43_1","doi-asserted-by":"publisher","DOI":"10.1145\/3339363.3339370"},{"key":"e_1_2_1_44_1","doi-asserted-by":"publisher","DOI":"10.1109\/TBDATA.2015.2465959"}],"container-title":["ACM Transactions on Intelligent Systems and Technology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3389127","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3389127","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T22:33:02Z","timestamp":1750199582000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3389127"}},"subtitle":["Collaborative Filtering via Pointwise Preference Learning on User\/Item-Set"],"short-title":[],"issued":{"date-parts":[[2020,5,25]]},"references-count":44,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2020,8,31]]}},"alternative-id":["10.1145\/3389127"],"URL":"https:\/\/doi.org\/10.1145\/3389127","relation":{},"ISSN":["2157-6904","2157-6912"],"issn-type":[{"type":"print","value":"2157-6904"},{"type":"electronic","value":"2157-6912"}],"subject":[],"published":{"date-parts":[[2020,5,25]]},"assertion":[{"value":"2019-12-01","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2020-03-01","order":1,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2020-05-25","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}