Abstract
In the recommender system, the user’s historical behavior data is one of the most important sources of the system’s input data. According to the user’s feedback mechanism, behavior data can be divided into explicit feedback data and implicit feedback data. However, most recommendation algorithms focus separately on explicit feedback or implicit feedback. How to combine explicit and implicit feedback for recommendation tasks has always been a research problem. In recent years, deep learning technology has dominated the research on recommendation algorithms. But even the latest neural network-based recommendation algorithm cannot exceed classic methods (such as matrix factorization) in most cases. In this work, we propose a new collaborative filtering framework with neural network architecture. On the one hand, we use both explicit feedback data and implicit feedback data as input to learn multiple representations of users and items. On the other hand, we use multi-task learning to optimize our framework and use two relatively simple auxiliary tasks to enhance the generalization ability of our framework. Extensive experiments on five real-world datasets show significant improvements in our proposed framework over the state-of-the-art methods and vanilla matrix factorization.
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References
Ebesu T, Shen B, Fang Y (2018) Collaborative memory network for recommendation systems. In: The 41st International ACM SIGIR Conference on Research and Development in Information Retrieval, pp 515–524
Liang D, Krishnan RG, Hoffman MD, Jebara T (2018) Variational autoencoders for collaborative filtering. In: Proceedings of the 2018 World Wide Web Conference, pp 689–698
Sarwar B, Karypis G, Konstan J, Riedl J (2001) Item-based collaborative filtering recommendation algorithms. In: Proceedings of the 10th International Conference on World Wide Web, pp 285–295
Zhang H, Shen F, Liu W, He X, Luan H, Chua T-S (2016) Discrete collaborative filtering. In: Proceedings of the 39th International ACM SIGIR conference on Research and Development in Information Retrieval, pp 325–334
He X, Zhang H, Kan M-Y, Chua T-S (2016) Fast matrix factorization for online recommendation with implicit feedback. In: Proceedings of the 39th International ACM SIGIR conference on Research and Development in Information Retrieval, pp 549–558
Hong-Jian X, Xinyu D, Jianbing Z, Shujian H, Jiajun C (2017) Deep matrix factorization models for recommender systems. IJCAI 17:3203–3209
He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 770–778
Serban I, Sordoni A, Bengio Y, Courville A, Pineau J (2016) Building end-to-end dialogue systems using generative hierarchical neural network models. In: Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence (AAAI 2016). AAAI Press, pp 3776–3783
Huang F, Li X, Yuan C, Zhang S, Zhang J, Qiao S (2021) Attention-emotion-enhanced convolutional lstm for sentiment analysis. IEEE Trans Neural Netw Learn Syst:1–14
Hornik K, Stinchcombe M, White H et al (1989) Multilayer feedforward networks are universal approximators. Neural Netw 2(5):359–366
He X, Liao L, Zhang H, Nie L, Hu X, Chua T-S (2017) Neural collaborative filtering. In: Proceedings of the 26th International Conference on World Wide Web, pp 173–182
Rendle S, Krichene W, Zhang L, Anderson J (2020) Neural collaborative filtering vs. matrix factorization revisited, pp 240–248
Van den Oord A, Dieleman S, Schrauwen B (2013) Deep content-based music recommendation. Advances in neural information processing systems. Springer, Berlin, pp 2643–2651
Wang H, Wang N, Yeung D-Y (2015) Collaborative deep learning for recommender systems. In: Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp 1235–1244
Zhang F, Yuan NJ, Lian D, Xie X, Ma W-Y (2016) Collaborative knowledge base embedding for recommender systems. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp 353–362
Alter O, Brown PO, Botstein D (2000) Singular value decomposition for genome-wide expression data processing and modeling. Proc Natl Acad Sci 97(18):10101–10106
Lee DD, Sebastian Seung H (1999) Learning the parts of objects by non-negative matrix factorization. Nature 401(6755):788–791
Salakhutdinov R, Mnih A, Hinton G (2007) Restricted boltzmann machines for collaborative filtering. In: Proceedings of the 24th International Conference on Machine Learning, pp 791–798
Georgiev K, Nakov P (2013) A non-iid framework for collaborative filtering with restricted boltzmann machines. In: International Conference on Machine Learning, pp 1148–1156
Koren Y (2008) Factorization meets the neighborhood: a multifaceted collaborative filtering model. In: Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp 426–434
Bai T, Wen J-R, Zhang J, Zhao WX (2017) A neural collaborative filtering model with interaction-based neighborhood. In: Proceedings of the 2017 ACM on Conference on Information and Knowledge Management, pp 1979–1982
Zhang S, Tay Y, Yao L, Sun A (2018) Next item recommendation with self-attention. CoRR. arXiv:1808.06414
Chen S, Peng Y (2018) Matrix factorization for recommendation with explicit and implicit feedback. Knowl-Based Syst 158:109–117
Liu NN, Xiang EW, Zhao M, Yang Q (2010) Unifying explicit and implicit feedback for collaborative filtering. In: Proceedings of the 19th ACM International Conference on Information and Knowledge Management, pp 1445–1448
Shi Y, Karatzoglou A, Baltrunas L, Larson M, Hanjalic A (2013) xclimf: optimizing expected reciprocal rank for data with multiple levels of relevance. In: Proceedings of the 7th ACM Conference on Recommender Systems, pp 431–434
Bell RM, Koren Y (2007) Scalable collaborative filtering with jointly derived neighborhood interpolation weights. In: Seventh IEEE International Conference on Data Mining (ICDM 2007), pp 43–52. IEEE
Pan W, Liu Z, Ming Z, Zhong H, Wang X, Congfu X (2015) Compressed knowledge transfer via factorization machine for heterogeneous collaborative recommendation. Knowl-Based Syst 85:234–244
Li G, Chen Q (2016) Exploiting explicit and implicit feedback for personalized ranking. Math Probl Eng 2016(11):2535329. https://doi.org/10.1155/2016/2535329
Chen BY, Huang L, Wang CD, Jing LP (2020) Explicit and implicit feedback based collaborative filtering algorithm. J Softw 3:794–805
Caruana R (1997) Multitask learning. Mach Learn 28(1):41–75
Xie Z, Cao W, Ming Z (2021) A further study on biologically inspired feature enhancement in zero-shot learning. Int J Mach Learn Cybern 12(1):257–269
Long L, Yin Y, Huang F (2021) Graph-aware collaborative filtering for top-n recommendation. In: 2021 International Joint Conference on Neural Networks (IJCNN), pp 1–8
Hadash G, Sar SO, Osadchy R (2018) Rank and rate: multi-task learning for recommender systems. In: Proceedings of the 12th ACM Conference on Recommender Systems, pp 451–454
Lu Y, Dong R, Smyth B (2018) Why i like it: multi-task learning for recommendation and explanation. In: Proceedings of the 12th ACM Conference on Recommender Systems, pp 4–12
Ma X, Zhao L, Huang G, Wang Z, Hu Z, Zhu X, Gai K (2018) Entire space multi-task model: an effective approach for estimating post-click conversion rate. In: The 41st International ACM SIGIR Conference on Research and Development in Information Retrieval, pp 1137–1140
Ni Y, Ou D, Liu S, Li X, Ou W, Zeng A, Si L (2018) Perceive your users in depth: Learning universal user representations from multiple e-commerce tasks. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp 596–605
Zhao Z, Hong L, Wei L, Chen J, Nath A, Andrews S, Kumthekar A, Sathiamoorthy M, Yi X, Chi E (2019) Recommending what video to watch next: a multitask ranking system. In: Proceedings of the 13th ACM Conference on Recommender Systems, pp 43–51
Deng Z-H, Huang L, Wang C-D, Lai J-H, Yu PS (2019) Deepcf: a unified framework of representation learning and matching function learning in recommender system. Proc AAAI Conf Artif Intell 33:61–68
Mnih A, Salakhutdinov RR (2008) Probabilistic matrix factorization. Advances in neural information processing systems. Springer, Berlin, pp 1257–1264
Koren Y, Bell R, Volinsky C (2009) Matrix factorization techniques for recommender systems. Computer 42(8):30–37
Salakhutdinov R, Mnih A (2008) Bayesian probabilistic matrix factorization using markov chain monte carlo. In: Proceedings of the 25th International Conference on Machine learning, pp 880–887
Kingma DP, Ba J (2014) Adam: A method for stochastic optimization. In: Proceedings of the 3rd International Conference on Learning Representations (ICLR). arXiv:1412.6980
Wang H, Zhang F, Zhao M, Li W, Xie X, Guo M (2019) Multi-task feature learning for knowledge graph enhanced recommendation. In: The World Wide Web Conference, pp 2000–2010
Xin X, He X, Zhang Y, Zhang Y, Jose J (2019) Relational collaborative filtering: Modeling multiple item relations for recommendation. In: Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval, pp 125–134
Bayer I, He X, Kanagal B, Rendle S (2017) A generic coordinate descent framework for learning from implicit feedback. In: Proceedings of the 26th International Conference on World Wide Web, pp 1341–1350
He X, Chen T, Kan M-Y, Chen X (2015) Trirank: Review-aware explainable recommendation by modeling aspects. In: Proceedings of the 24th ACM International on Conference on Information and Knowledge Management, pp 1661–1670
Tan YK, Xu X, Liu Y (2016) Improved recurrent neural networks for session-based recommendations. In: Proceedings of the 1st Workshop on Deep Learning for Recommender Systems, pp 17–22
Cao W, Wang X, Ming Z, Gao J (2018) A review on neural networks with random weights. Neurocomputing 275:278–287
Cao W, Hu L, Gao J, Wang X, Ming Z (2020) A study on the relationship between the rank of input data and the performance of random weight neural network. Neural Comput Appl:1–12
Wang X, Cao W (2018) Non-iterative approaches in training feed-forward neural networks and their applications. Soft computing-a fusion of foundations, methodologies and applications 22(11):3473–3476
Rendle S, Freudenthaler C, Gantner Z, Schmidt-Thieme L (2009) Bpr: Bayesian personalized ranking from implicit feedback. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp 452–461
Socher R, Chen D, Manning CD, Ng A (2013) Reasoning with neural tensor networks for knowledge base completion. Advances in neural information processing systems. Springer, Berlin, pp 926–934
Funding
This research was funded by Natural Science Foundation of China, Grant nos [61962038, 61962038] and Guangxi Bagui Teams for Innovation and Research, Grant no [2019].
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Long, L., Huang, F., Yin, Y. et al. Multi-task learning for collaborative filtering. Int. J. Mach. Learn. & Cyber. 13, 1355–1368 (2022). https://doi.org/10.1007/s13042-021-01451-0
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DOI: https://doi.org/10.1007/s13042-021-01451-0