Xiancong Chen
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Abstract
Recommender systems play an important role in providing personalized services for users in the context of information overload. Generally, users’ feedback toward items often contain the most significant information reflecting their preferences, which enables accurate personalized recommendation. In real applications, users’ feedback are usually heterogeneous (rather than homogeneous) such as purchases and examinations in e-commerce, which reflects users’ 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., purchases) with the assistance of the auxiliary feedback (e.g., examinations). 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.
- Pierre Baldi and Kurt Hornik. 1989. Neural networks and principal component analysis: Learning from examples without local minima. Neural Netw. 2, 1 (1989), 53--58.Google Scholar
Digital Library
- 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’13). 2787--2795.Google Scholar
- Yixin Cao, Xiang Wang, Xiangnan He, Zikun Hu, and Tat-Seng Chua. 2019. Unifying knowledge graph learning and recommendation: Towards a better understanding of user preferences. In Proceedings of the 28th International Conference on World Wide Web (WWW’19). 151--161.Google ScholarDigital Library
- Chong Chen, Min Zhang, Weizhi Ma, Yongfeng Zhang, Yiqun Liu, and Shaoping Ma. 2020. Efficient heterogeneous collaborative filtering without negative sampling for recommendation. In Proceedings of the 34th AAAI Conference on Artificial Intelligence (AAAI’20).Google ScholarCross Ref
- Lini Chen, Xin Xin, Dong Wong, and Yue Ding. 2017. HCoM: Item-based similarity model for heterogeneous implicit feedback. In Proceedings of the 18th IEEE International Conference on Mobile Data Management (MDM’17). 40--49.Google ScholarCross Ref
- Xinshi Chen, Shuang Li, Hui Li, Shaohua Jiang, Yuan Qi, and Le Song. 2019. Generative adversarial user model for reinforcement learning based recommendation system. In Proceedings of the 36th International Conference on Machine Learning (ICML’19). 1052--1061.Google Scholar
- Xu Chen, Hongteng Xu, Yongfeng Zhang, Jiaxi Tang, Yixin Cao, Zheng Qin, and Hongyuan Zha. 2018. Sequential recommendation with user memory networks. In Proceedings of the 11th ACM International Conference on Web Search and Data Mining (WSDM’18). 108--116.Google ScholarDigital Library
- Heng-Tze Cheng, Levent Koc, Jeremiah Harmsen, Tal Shaked, Tushar Chandra, Hrishi Aradhye, Glen Anderson, Greg Corrado, Wei Chai, Mustafa Ispir, Rohan Anil, Zakaria Haque, Lichan Hong, Vihan Jain, Xiaobing Liu, and Hemal Shah. 2016. Wide and deep learning for recommender systems. In Proceedings of the 1st Workshop on Deep Learning for Recommender Systems. 7--10.Google ScholarDigital Library
- Jian Cheng, Ting Yuan, Jinqiao Wang, and Hanqing Lu. 2014. Group latent factor model for recommendation with multiple user behaviors. In Proceedings of the 37th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR’14). 995--998.Google ScholarDigital Library
- Paolo Cremonesi, Antonio Tripodi, and Roberto Turrin. 2011. Cross-domain recommender systems. In Proceedings of the 11th IEEE International Conference on Data Mining Workshops. 496--503.Google ScholarDigital Library
- Kenan Cui, Xu Chen, Jiangchao Yao, and Ya Zhang. 2018. Variational collaborative learning for user probabilistic representation. arXiv preprint arXiv:1809.08400.Google Scholar
- Maurizio Ferrari Dacrema, Paolo Cremonesi, and Dietmar Jannach. 2019. Are we really making much progress? A worrying analysis of recent neural recommendation approaches. In Proceedings of the 13th ACM Conference on Recommender Systems (RecSys’19). 101--109.Google ScholarDigital Library
- 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.2019.2931327Google Scholar
- Jingtao Ding, Guanghui Yu, Xiangnan He, Yuhan Quan, Yong Li, Tat-Seng Chua, Depeng Jin, and Jiajie Yu. 2018. Improving implicit recommender systems with view data. In Proceedings of the 27th International Joint Conference on Artificial Intelligence (IJCAI’18). 3343--3349.Google ScholarDigital Library
- Jingtao Ding, Guanghui Yu, Yong Li, Xiangnan He, and Depeng Jin. 2020b. Improving implicit recommender systems with auxiliary data. ACM Trans. Info. Syst. 38, 1 (2020), 11:1–11:27.Google ScholarDigital Library
- Travis Ebesu, Bin Shen, and Yi Fang. 2018. Collaborative memory network for recommendation systems. In Proceedings of the 41st International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR’18). 515--524.Google ScholarDigital Library
- Martin J. Eppler and Jeanne Mengis. 2004. The concept of information overload: A review of literature from organization science, accounting, marketing, MIS, and related disciplines. Info. Soc. 20, 5 (2004), 325--344.Google Scholar
- Jun Feng, Heng Li, Minlie Huang, Shichen Liu, Wenwu Ou, Zhirong Wang, and Xiaoyan Zhu. 2018. Learning to collaborate: Multi-scenario ranking via multi-agent reinforcement learning. In Proceedings of the 27th International Conference on World Wide Web (WWW’18). 1939--1948.Google ScholarDigital Library
- Chen Gao, Xiangning Chen, Fuli Feng, Kai Zhao, Xiangnan He, Yong Li, and Depeng Jin. 2019a. Cross-domain recommendation without sharing user-relevant data. In Proceedings of the 28th International Conference on World Wide Web (WWW’19). 491--502.Google ScholarDigital Library
- Chen Gao, Xiangnan He, Danhua Gan, Xiangning Chen, Fuli Feng, Yong Li, Tat-Seng Chua, Lina Yao, Yang Song, and Depeng Jin. 2020. Learning to recommend with multiple cascading behaviors. IEEE Trans. Knowl. Data Eng. (2020). DOI:https://doi.org/10.1109/TKDE.2019.2958808Google Scholar
- Chen Gao, Chao Huang, Yue Yu, Huandong Wang, Yong Li, and Depeng Jin. 2019b. Privacy-preserving cross-domain location recommendation. Proc. ACM Interact. Mobile Wear. Ubiq. Technol. 3, 1 (2019), 11:1–11:21.Google ScholarDigital Library
- Guibing Guo, Huihuai Qiu, Zhenhua Tan, Yuan Liu, Jing Ma, and Xingwei Wang. 2017a. Resolving data sparsity by multi-type auxiliary implicit feedback for recommender systems. Knowl.-Based Syst. 138 (2017), 202--207.Google ScholarDigital Library
- Huifeng Guo, Ruiming Tang, Yunming Ye, Zhenguo Li, and Xiuqiang He. 2017b. DeepFM: A factorization-machine based neural network for CTR prediction. In Proceedings of the 26th International Joint Conference on Artificial Intelligence (IJCAI’17). 1725--1731.Google ScholarDigital Library
- F. Maxwell Harper and Joseph A. Konstan. 2015. The MovieLens datasets: History and context. ACM Trans. Interact. Intell. Syst. 5, 4 (2015), 19:1–19:19.Google Scholar
- Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. 2016. Deep residual learning for image recognition. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR’16). 770--778.Google ScholarCross Ref
- Xiangnan He, Zhankui He, Jingkuan Song, Zhenguang Liu, Yu-Gang Jiang, and Tat-Seng Chua. 2018. NAIS: Neural attentive item similarity model for recommendation. IEEE Trans. Knowl. Data Eng. 30, 12 (2018), 2354--2366.Google ScholarDigital Library
- Xiangnan He, Lizi Liao, Hanwang Zhang, Liqiang Nie, Xia Hu, and Tat-Seng Chua. 2017. Neural collaborative filtering. In Proceedings of the 26th International Conference on World Wide Web (WWW’17). 173--182.Google ScholarDigital Library
- Balázs Hidasi, Alexandros Karatzoglou, Linas Baltrunas, and Domonkos Tikk. 2015. Session-based recommendations with recurrent neural networks. arXiv preprint arXiv:1511.06939.Google Scholar
- Yifan Hu, Koren Yehuda, and Volinsky Chris. 2008. Collaborative filtering for implicit feedback datasets. In Proceedings of the 8th IEEE International Conference on Data Mining (ICDM’08). 263--272.Google ScholarDigital Library
- Joseph G. Ibrahim, Ming-Hui Chen, and Debajyoti Sinha. 2001. Bayesian Survival Analysis. Springer.Google Scholar
- Dietmar Jannach and Malte Ludewig. 2017. When recurrent neural networks meet the neighborhood for session-based recommendation. In Proceedings of the 11th ACM Conference on Recommender Systems (RecSys’17). 306--310.Google ScholarDigital Library
- Christopher C. Johnson. 2014. Logistic matrix factorization for implicit feedback data. In Proceedings of the Workshop on Distributed Machine Learning and Matrix Computations at NeurIPS 2014.Google Scholar
- Santosh Kabbur, Xia Ning, and George Karypis. 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 (KDD’13). 659--667.Google ScholarDigital Library
- Leslie Pack Kaelbling, Michael L. Littman, and Andrew W. Moore. 1996. Reinforcement learning: A survey. J. Artific. Intell. Res. 4 (1996), 237--285.Google ScholarDigital Library
- Wang-Cheng Kang and Julian McAuley. 2018. Self-attentive sequential recommendation. In Proceedings of the 18th IEEE International Conference on Data Mining (ICDM’18). 197--206.Google ScholarCross Ref
- Yoon Kim. 2014. Convolutional neural networks for sentence classification. arXiv preprint arXiv:1408.5882.Google Scholar
- Mark A. Kramer. 1991. Nonlinear principal component analysis using autoassociative neural networks. AIChE J. 37, 2 (1991), 233--243.Google ScholarCross Ref
- Wonsung Lee, Kyungwoo Song, and Il-Chul Moon. 2017. Augmented variational autoencoders for collaborative filtering with auxiliary information. In Proceedings of the 26th ACM International Conference on Information and Knowledge Management (CIKM’17). 1139--1148.Google ScholarDigital Library
- Lukas Lerche and Dietmar Jannach. 2014. Using graded implicit feedback for Bayesian personalized ranking. In Proceedings of the 8th ACM Conference on Recommender Systems (RecSys’14). 353--356.Google ScholarDigital Library
- Bin Li. 2011. Cross-domain collaborative filtering: A brief survey. In Proceedings of the 23rd IEEE International Conference on Tools with Artificial Intelligence (ICTAI’11). 1085--1086.Google ScholarDigital Library
- Chenliang Li, Cong Quan, Li Peng, Yunwei Qi, Yuming Deng, and Libing Wu. 2019. A capsule network for recommendation and explaining what You like and dislike. In Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR’19). 275--284.Google ScholarDigital Library
- Hang Li. 2017. Deep learning for natural language processing: Advantages and challenges. National Sci. Rev. 5, 1 (09 2017), 24--26.Google Scholar
- Zhi Li, Hongke Zhao, Qi Liu, Zhenya Huang, Tao Mei, and Enhong Chen. 2018. Learning from history and present: Next-item recommendation via discriminatively exploiting user behaviors. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD’18). 1734--1743.Google ScholarDigital Library
- Dawen Liang, Rahul G. Krishnan, Matthew D. Hoffman, and Tony Jebara. 2018. Variational autoencoders for collaborative filtering. In Proceedings of the 27th International Conference on World Wide Web (WWW’18). 689--698.Google ScholarDigital Library
- Guanyu Lin, Feng Liang, Weike Pan, and Zhong Ming. 2019. FedRec: Federated recommendation with explicit feedback via user averaging. In Proceedings of the 1st International Workshop on Federated Machine Learning for User Privacy and Data Confidentiality (FML’19) co-located with the 28th International Joint Conference on Artificial Intelligence (IJCAI’19).Google Scholar
- Feng Liu, Ruiming Tang, Xutao Li, Weinan Zhang, Yunming Ye, Haokun Chen, Huifeng Guo, and Yuzhou Zhang. 2018a. Deep reinforcement learning based recommendation with explicit user-item interactions modeling. arXiv preprint arXiv:1810.12027.Google Scholar
- Qiang Liu, Shu Wu, and Liang Wang. 2017. Multi-behavioral sequential prediction with recurrent log-bilinear model. IEEE Trans. Knowl. Data Eng. 29, 6 (2017), 1254--1267.Google ScholarDigital Library
- Siyuan Liu, Qiong Wu, and Chunyan Miao. 2018b. Personalized recommendation considering secondary implicit feedback. In Proceedings of the 3rd IEEE International Conference on Agents (ICA’18). 87--92.Google ScholarCross Ref
- Babak Loni, Roberto Pagano, Martha Larson, and Alan Hanjalic. 2016. Bayesian personalized ranking with multi-channel user feedback. In Proceedings of the 10th ACM Conference on Recommender Systems (RecSys’16). 361--364.Google ScholarDigital Library
- Babak Loni, Roberto Pagano, Martha Larson, and Alan Hanjalic. 2019. Top-N recommendation with multi-channel positive feedback using factorization machines. ACM Trans. Info. Syst. 37, 2 (2019), 15:1–15:23.Google ScholarDigital Library
- Weizhi Ma, Min Zhang, Yue Cao, Woojeong Jin, Chenyang Wang, Yiqun Liu, Shaoping Ma, and Xiang Ren. 2019. Jointly learning explainable rules for recommendation with knowledge graph. In Proceedings of the 28th International Conference on World Wide Web Conference (WWW’19). 1210--1221.Google ScholarDigital Library
- Brendan McMahan, Eider Moore, Daniel Ramage, Seth Hampson, and Blaise Agüera y Arcas. 2017. Communication-efficient learning of deep networks from decentralized data. In Proceedings of the 20th International Conference on Artificial Intelligence and Statistics (AISTATS’17). 1273--1282.Google Scholar
- Tomáš Mikolov, Martin Karafiát, Lukáš Burget, Jan Černockỳ, and Sanjeev Khudanpur. 2010. Recurrent neural network based language model. In Proceedings of the 11th Annual Conference of the International Speech Communication Association (ISCA’10). 1045--1048.Google ScholarCross Ref
- Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg Corrado, and Jeffrey Dean. 2013. Distributed representations of words and phrases and their compositionality. In Proceedings of the 26th International Conference on Neural Information Processing Systems (NeurIPS’13). 3111--3119.Google Scholar
- Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Andrei A Rusu, Joel Veness, Marc G Bellemare, Alex Graves, Martin Riedmiller, Andreas K. Fidjeland, Georg Ostrovski, et al. 2015. Human-level control through deep reinforcement learning. Nature 518, 7540 (2015), 529--533.Google Scholar
- Xia Ning and George Karypis. 2011. SLIM: Sparse linear methods for top-N recommender systems. In Proceedings of the 11th IEEE International Conference on Data Mining (ICDM’11). 497--506.Google ScholarDigital Library
- Yuanxin Ouyang, Wenqi Liu, Wenge Rong, and Zhang Xiong. 2014. Autoencoder-based collaborative filtering. In Proceedings of the 27th International Conference on Neural Information Processing (NeurIPS’14). Springer, 284--291.Google ScholarCross Ref
- Rong Pan, Yunhong Zhou, Bin Cao, Nathan N. Liu, Rajan Lukose, Martin Scholz, and Qiang Yang. 2008. One-class collaborative filtering. In Proceedings of the 8th IEEE International Conference on Data Mining (ICDM’08). 502--511.Google ScholarDigital Library
- Sinno Jialin Pan and Qiang Yang. 2010. A survey on transfer learning. IEEE Trans. Knowl. Data Eng. 22, 10 (2010), 1345--1359.Google ScholarDigital Library
- Weike Pan. 2016. A survey of transfer learning for collaborative recommendation with auxiliary data. Neurocomputing 177 (2016), 447--453.Google ScholarDigital Library
- Weike Pan and Li Chen. 2013. CoFiSet: Collaborative filtering via learning pairwise preferences over item-sets. In Proceedings of the 13th SIAM International Conference on Data Mining (SDM’13). 180--188.Google ScholarCross Ref
- Weike Pan and Guangneng Hu. 2020. Transfer Learning in Recommender Systems. Cambridge University Press. 279–288 pages.Google Scholar
- Weike Pan, Mengsi Liu, and Zhong Ming. 2016. Transfer learning for heterogeneous one-class collaborative filtering. IEEE Intell. Syst. 31, 4 (2016), 43--49.Google ScholarDigital Library
- Weike Pan, Qiang Yang, Wanling Cai, Yaofeng Chen, Qing Zhang, Xiaogang Peng, and Zhong Ming. 2019. Transfer to rank for heterogeneous one-class collaborative filtering. ACM Trans. Info. Syst. 37, 1 (2019), 10:1–10:20.Google ScholarDigital Library
- Weike Pan, Hao Zhong, Congfu Xu, and Zhong Ming. 2015. Adaptive Bayesian personalized ranking for heterogeneous implicit feedbacks. Knowl.-Based Syst. 73 (2015), 173--180.Google ScholarDigital Library
- Bo Pang, Kaiwen Zha, Hanwen Cao, Chen Shi, and Cewu Lu. 2019. Deep RNN framework for visual sequential applications. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR’19). 423--432.Google ScholarCross Ref
- Changhua Pei, Xinru Yang, Qing Cui, Xiao Lin, Fei Sun, Peng Jiang, Wenwu Ou, and Yongfeng Zhang. 2019. Value-aware recommendation based on reinforcement profit maximization. In Proceedings of the 28th International Conference on World Wide Web (WWW’19). 3123--3129.Google ScholarDigital Library
- Xiaogang Peng, Yaofeng Chen, Yuchao Duan, Weike Pan, and Zhong Ming. 2016. RBPR: Role-based Bayesian personalized ranking for heterogeneous one-class collaborative filtering. In Proceedings of the 24th ACM Conference on User Modeling, Adaptation and Personalisation (UMAP’16).Google Scholar
- Huihuai Qiu, Yun Liu, Guibing Guo, Zhu Sun, Jie Zhang, and Hai Thanh Nguyen. 2018. BPRH: Bayesian personalized ranking for heterogeneous implicit feedback. Info. Sci. 453 (2018), 80--98.Google Scholar
- Steffen Rendle and Christoph Freudenthaler. 2014. Improving pairwise learning for item recommendation from implicit feedback. In Proceedings of the 7th ACM International Conference on Web Search and Data Mining (WSDM’14). 273–282.Google ScholarDigital Library
- Steffen Rendle, Christoph Freudenthaler, Zeno Gantner, and Schmidt-Thie Lars. 2009. BPR: Bayesian personalized ranking from implicit feedback. In Proceedings of the 25th Conference on Uncertainty in Artificial Intelligence (UAI’09). 452–461.Google ScholarDigital Library
- Francesco Ricci, Lior Rokach, and Bracha Shapira. 2015. Recommender Systems Handbook (2nd ed.). Springer.Google ScholarDigital Library
- David E. Rumelhart, Geoffrey E. Hinton, and Ronald J. Williams. 1985. Learning Internal Representations by Error Propagation. Technical Report.Google Scholar
- Ruslan Salakhutdinov and Andriy Mnih. 2007. Probabilistic matrix factorization. In Proceedings of the 20th International Conference on Neural Information Processing Systems (NeurIPS’07). 1257--1264.Google ScholarDigital Library
- Suvash Sedhain, Aditya Krishna Menon, Scott Sanner, and Lexing Xie. 2015. AutoRec: Autoencoders meet collaborative filtering. In Proceedings of the 24th International Conference on World Wide Web (WWW’15). 111--112.Google ScholarDigital Library
- Chuan Shi, Jian Liu, Yiding Zhang, Binbin Hu, Shenghua Liu, and Philip S. Yu. 2018. MFPR: A personalized ranking recommendation with multiple feedback. ACM Trans. Soc. Comput. 1, 2 (2018), 7:1–7:22.Google ScholarDigital Library
- Ajit Paul Singh and Geoffrey J. Gordon. 2008. Relational learning via collective matrix factorization. In Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD’08). 650--658.Google Scholar
- Kihyuk Sohn, Honglak Lee, and Xinchen Yan. 2015. Learning structured output representation using deep conditional generative models. In Proceedings of the 28th International Conference on Neural Information Processing Systems (NeurIPS’15). 3483--3491.Google Scholar
- Bo Song, Xin Yang, Yi Cao, and Congfu Xu. 2018. Neural collaborative ranking. In Proceedings of the 27th ACM International Conference on Information and Knowledge Management (CIKM’18). 1353--1362.Google ScholarDigital Library
- Yang Song, Ali Mamdouh Elkahky, and Xiaodong He. 2016. Multi-rate deep learning for temporal recommendation. In Proceedings of the 39th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR’16). 909--912.Google ScholarDigital Library
- Sainbayar Sukhbaatar, Jason Weston, Rob Fergus, et al. 2015. End-to-end memory networks. In Proceedings of the 28th International Conference on Neural Information Processing Systems (NeurIPS’15). 2440--2448.Google Scholar
- Jiaxi Tang, Francois Belletti, Sagar Jain, Minmin Chen, Alex Beutel, Can Xu, and Ed H. Chi. 2019. Towards neural mixture recommender for long range dependent user sequences. In Proceedings of the 28th International Conference on World Wide Web (WWW’19). 1782--1793.Google Scholar
- Jiaxi Tang and Ke Wang. 2018. Personalized top-N sequential recommendation via convolutional sequence embedding. In Proceedings of the 11th ACM International Conference on Web Search and Data Mining (WSDM’18). 565--573.Google ScholarDigital Library
- Liang Tang, Bo Long, Bee-Chung Chen, and Deepak Agarwal. 2016. An empirical study on recommendation with multiple types of feedback. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD’16). 283--292.Google ScholarDigital Library
- Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, and Illia Polosukhin. 2017. Attention is all you need. In Proceedings of the 30th International Conference on Neural Information Processing Systems (NeurIPS’17). 5998--6008.Google Scholar
- Mengting Wan and Julian McAuley. 2018. Item recommendation on monotonic behavior chains. In Proceedings of the 12th ACM Conference on Recommender Systems (RecSys’18). 86--94.Google ScholarDigital Library
- Hao Wang, Naiyan Wang, and Dit-Yan Yeung. 2015. Collaborative deep learning for recommender systems. In Proceedings of the 21st ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD’15). 1235--1244.Google ScholarDigital Library
- Jing Wang, Lan-fen Lin, Heng Zhang, Jia-qi Tu, and Peng-hua Yu. 2017. A novel confidence estimation method for heterogeneous implicit feedback. Front. Info. Technol. Electron. Eng. 18, 11 (2017), 1817--1827.Google ScholarCross Ref
- Shoujin Wang, Liang Hu, Yan Wang, Longbing Cao, Quan Z. Sheng, and Mehmet Orgun. 2019a. Sequential recommender systems: Challenges, progress and prospects. In Proceedings of the 28th International Joint Conference on Artificial Intelligence (IJCAI’19). 6332--6338.Google ScholarCross Ref
- Xiang Wang, Dingxian Wang, Canran Xu, Xiangnan He, Yixin Cao, and Tat-Seng Chua. 2019b. Explainable reasoning over knowledge graphs for recommendation. Proceedings of the 33rd AAAI Conference on Artificial Intelligence 33, 1 (2019), 5329--5336.Google ScholarCross Ref
- Hongfa Wen, Xin Liu, Chenggang Yan, Linhua Jiang, Yaoqi Sun, Jiyong Zhang, and Haibing Yin. 2019. Leveraging multiple implicit feedback for personalized recommendation with neural network. In Proceedings of the 1st International Conference on Artificial Intelligence and Advanced Manufacturing (AIAM’19).Google ScholarDigital Library
- Jason Weston, Sumit Chopra, and Antoine Bordes. 2014. Memory networks. arXiv preprint arXiv:1410.3916.Google Scholar
- 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 (ICML’18). 5311--5320.Google Scholar
- Yao Wu, Christopher DuBois, Alice X. Zheng, and Martin Ester. 2016. Collaborative denoising auto-encoders for top-N recommender systems. In Proceedings of the 9th ACM International Conference on Web Search and Data Mining (WSDM’16). 153--162.Google ScholarDigital Library
- Fen Xia, Tie-Yan Liu, Jue Wang, Wensheng Zhang, and Hang Li. 2008. Listwise approach to learning to rank: Theory and algorithm. In Proceedings of the 25th International Conference on Machine Learning (ICML’08). 1192--1199.Google ScholarDigital Library
- Jun Xiao, Hao Ye, Xiangnan He, Hanwang Zhang, Fei Wu, and Tat-Seng Chua. 2017. Attentional factorization machines: Learning the weight of feature interactions via attention networks. In Proceedings of the 26th International Joint Conference on Artificial Intelligence (IJCAI’17). 3119--3125.Google ScholarDigital Library
- Feng Xue, Xiangnan He, Xiang Wang, Jiandong Xu, Kai Liu, and Richang Hong. 2019. Deep item-based collaborative filtering for top-N recommendation. ACM Trans. Info. Syst. 37, 3 (2019), 33.Google ScholarDigital Library
- Hong-Jian Xue, Xinyu Dai, Jianbing Zhang, Shujian Huang, and Jiajun Chen. 2017. Deep matrix factorization models for recommender systems. In Proceedings of the 26th International Joint Conference on Artificial Intelligence (IJCAI’17). 3203--3209.Google ScholarCross Ref
- Qiang Yang, Yang Liu, Tianjian Chen, and Yongxin Tong. 2019. Federated machine learning: Concept and applications. ACM Trans. Intell. Syst. Technol. 10, 2 (2019), 12:1–12:19.Google ScholarDigital Library
- Hongzhi Yin, Hongxu Chen, Xiaoshuai Sun, Hao Wang, Yang Wang, and Quoc Viet Hung Nguyen. 2017. SPTF: A scalable probabilistic tensor factorization model for semantic-aware behavior prediction. In Proceedings of the 17th IEEE International Conference on Data Mining (ICDM’17). 585--594.Google ScholarCross Ref
- Shuai Zhang, Lina Yao, Aixin Sun, and Yi Tay. 2019. Deep learning based recommender system: A survey and new perspectives. Comput. Surveys 52, 1 (2019), 5:1–5:38.Google ScholarDigital Library
- Xiangyu Zhao, Long Xia, Liang Zhang, Zhuoye Ding, Dawei Yin, and Jiliang Tang. 2018a. Deep reinforcement learning for page-wise recommendations. In Proceedings of the 12th ACM Conference on Recommender Systems (RecSys’18). 95–103.Google ScholarDigital Library
- Xiangyu Zhao, Liang Zhang, Zhuoye Ding, Long Xia, Jiliang Tang, and Dawei Yin. 2018b. Recommendations with negative feedback via pairwise deep reinforcement learning. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD’18). 1040--1048.Google ScholarDigital Library
- Xiangyu Zhao, Liang Zhang, Long Xia, Zhuoye Ding, Dawei Yin, and Jiliang Tang. 2017. Deep reinforcement learning for list-wise recommendations. arXiv preprint arXiv:1801.00209.Google Scholar
- Zhe Zhao, Zhiyuan Cheng, Lichan Hong, and Ed H. Chi. 2015. Improving user topic interest profiles by behavior factorization. In Proceedings of the 24th International Conference on World Wide Web (WWW’15). 1406--1416.Google Scholar
- Guanjie Zheng, Fuzheng Zhang, Zihan Zheng, Yang Xiang, Nicholas Jing Yuan, Xing Xie, and Zhenhui Li. 2018. DRN: A deep reinforcement learning framework for news recommendation. In Proceedings of the 27th International Conference on World Wide Web (WWW’18). 167--176.Google ScholarDigital Library
- Meizi Zhou, Zhuoye Ding, Jiliang Tang, and Dawei Yin. 2018. Micro behaviors: A new perspective in e-commerce recommender systems. In Proceedings of the 11th ACM International Conference on Web Search and Data Mining (WSDM’18). 727--735.Google ScholarDigital Library
- Xin Zhou, Guibing Guo, Zhu Sun, and Yuan Liu. 2020. Multi-facet user preference learning for fine-grained item recommendation. Neurocomputing 385 (2020), 258--268.Google ScholarDigital Library
- Xiao Zhou, Danyang Liu, Jianxun Lian, and Xing Xie. 2019. Collaborative metric learning with memory network for multi-relational recommender systems. In Proceedings of the 28th International Joint Conference on Artificial Intelligence (IJCAI’19). 4454--4460.Google ScholarCross Ref
- Lixin Zou, Long Xia, Zhuoye Ding, Jiaxing Song, Weidong Liu, and Dawei Yin. 2019. Reinforcement learning to optimize long-term user engagement in recommender systems. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD’19). 2810--2818.Google ScholarDigital Library
- Lixin Zou, Long Xia, Pan Du, Zhuo Zhang, Ting Bai, Weidong Liu, Jian-Yun Nie, and Dawei Yin. 2020. Pseudo Dyna-Q: A reinforcement learning framework for interactive recommendation. In Proceedings of the 13th International Conference on Web Search and Data Mining (WSDM’20). 816--824.Google ScholarDigital Library
Index Terms
- A Survey on Heterogeneous One-class Collaborative Filtering
Recommendations
Neural Collaborative Filtering
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