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Syst."],"published-print":{"date-parts":[[2020,10,31]]},"abstract":"<jats:p>\n            Recommender systems play an important role in providing personalized services for users in the context of information overload. Generally, users\u2019 feedback toward items often contain the most significant information reflecting their preferences, which enables accurate personalized recommendation. In real applications, users\u2019 feedback are usually heterogeneous (rather than homogeneous) such as\n            <jats:italic>purchases<\/jats:italic>\n            and\n            <jats:italic>examinations<\/jats:italic>\n            in e-commerce, which reflects users\u2019 preferences in different degrees. Effective modeling of such heterogeneous one-class feedback is challenging compared with that of homogeneous feedback of ratings. As a response, heterogeneous one-class collaborative filtering (HOCCF) is proposed, which often converts the heterogeneous feedback into two parts (i.e., target feedback and auxiliary feedback), aiming to care more about the target feedback (e.g.,\n            <jats:italic>purchases<\/jats:italic>\n            ) with the assistance of the auxiliary feedback (e.g.,\n            <jats:italic>examinations<\/jats:italic>\n            ). In this survey, we provide an overview of the representative HOCCF methods from the perspective of factorization-based methods, transfer learning-based methods, and deep learning-based methods. First, we review the factorization-based methods according to different strategies. Second, we describe the transfer learning-based methods with different knowledge sharing manners. Third, we discuss the deep learning-based methods according to the neural architectures. Moreover, we include some important example applications, describe the empirical studies, and discuss some promising future directions.\n          <\/jats:p>","DOI":"10.1145\/3402521","type":"journal-article","created":{"date-parts":[[2020,8,11]],"date-time":"2020-08-11T10:04:59Z","timestamp":1597140299000},"page":"1-54","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":15,"title":["A Survey on Heterogeneous One-class Collaborative Filtering"],"prefix":"10.1145","volume":"38","author":[{"given":"Xiancong","family":"Chen","sequence":"first","affiliation":[{"name":"College of Computer Science and Software Engineering and National Engineering Laboratory for Big Data System Computing Technology, Shenzhen University, Shenzhen"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lin","family":"Li","sequence":"additional","affiliation":[{"name":"College of Computer Science and Software Engineering and National Engineering Laboratory for Big Data System Computing Technology, Shenzhen University, Shenzhen"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"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"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"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"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2020,8,11]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1016\/0893-6080(89)90014-2"},{"key":"e_1_2_1_2_1","volume-title":"Proceedings of the 26th International Conference on Neural Information Processing Systems (NeurIPS\u201913)","author":"Bordes Antoine","year":"2013","unstructured":"Antoine Bordes , Nicolas Usunier , Alberto Garcia-Duran , Jason Weston , and Oksana Yakhnenko . 2013 . Translating embeddings for modeling multi-relational data . In Proceedings of the 26th International Conference on Neural Information Processing Systems (NeurIPS\u201913) . 2787--2795. Antoine Bordes, Nicolas Usunier, Alberto Garcia-Duran, Jason Weston, and Oksana Yakhnenko. 2013. Translating embeddings for modeling multi-relational data. In Proceedings of the 26th International Conference on Neural Information Processing Systems (NeurIPS\u201913). 2787--2795."},{"key":"e_1_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1145\/3308558.3313705"},{"key":"e_1_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i01.5329"},{"key":"e_1_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1109\/MDM.2017.16"},{"key":"e_1_2_1_6_1","volume-title":"Proceedings of the 36th International Conference on Machine Learning (ICML\u201919)","author":"Chen Xinshi","year":"2019","unstructured":"Xinshi Chen , Shuang Li , Hui Li , Shaohua Jiang , Yuan Qi , and Le Song . 2019 . 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Kenan Cui Xu Chen Jiangchao Yao and Ya Zhang. 2018. Variational collaborative learning for user probabilistic representation. arXiv preprint arXiv:1809.08400."},{"key":"e_1_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1145\/3298689.3347058"},{"key":"e_1_2_1_13_1","volume-title":"Sampler design for Bayesian personalized ranking by leveraging view data","author":"Ding Jingtao","year":"2020","unstructured":"Jingtao Ding , Guanghui Yu , Xiangnan He , Fuli Feng , Yong Li , and Depeng Jin . 2020a. Sampler design for Bayesian personalized ranking by leveraging view data . IEEE Trans. Knowl. Data Eng . ( 2020 ). DOI:https:\/\/doi.org\/10.1109\/TKDE.2019.2931327 10.1109\/TKDE.2019.2931327 Jingtao Ding, Guanghui Yu, Xiangnan He, Fuli Feng, Yong Li, and Depeng Jin. 2020a. Sampler design for Bayesian personalized ranking by leveraging view data. IEEE Trans. Knowl. Data Eng. (2020). 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