ABSTRACT
In this paper, we propose a Joint Collaborative Autoencoder framework that learns both user-user and item-item correlations simultaneously, leading to a more robust model and improved top-K recommendation performance. More specifically, we show how to model these user-item correlations and demonstrate the importance of careful normalization to alleviate the influence of feedback heterogeneity. Further, we adopt a pairwise hinge-based objective function to maximize the top-K precision and recall directly for top-K recommenders. Finally, a mini-batch optimization algorithm is proposed to train the proposed model. Extensive experiments on three public datasets show the effectiveness of the proposed framework over state-of-the-art non-neural and neural alternatives.
- Martín Abadi, Paul Barham, Jianmin Chen, Zhifeng Chen, Andy Davis, Jeffrey Dean, Matthieu Devin, Sanjay Ghemawat, Geoffrey Irving, Michael Isard, 2016. Tensorflow: a system for large-scale machine learning.. In OSDI, Vol. 16. 265-283. Google ScholarDigital Library
- Paul Covington, Jay Adams, and Emre Sargin. 2016. Deep neural networks for youtube recommendations. In Proceedings of the 10th ACM Conference on Recommender Systems. ACM, 191-198. Google ScholarDigital Library
- Ali Mamdouh Elkahky, Yang Song, and Xiaodong He. 2015. A multi-view deep learning approach for cross domain user modeling in recommendation systems. In Proceedings of the 24th International Conference on World Wide Web. International World Wide Web Conferences Steering Committee, 278-288. Google ScholarDigital Library
- Kostadin Georgiev and Preslav Nakov. 2013. A non-iid framework for collaborative filtering with restricted boltzmann machines. In International Conference on Machine Learning. 1148-1156. 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. International World Wide Web Conferences Steering Committee, 173-182. Google ScholarDigital Library
- Yifan Hu, Yehuda Koren, and Chris Volinsky. 2008. Collaborative filtering for implicit feedback datasets. In Data Mining, 2008. ICDM'08. Eighth IEEE International Conference on. Ieee, 263-272. Google ScholarDigital Library
- 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. ACM, 659-667. Google ScholarDigital Library
- Diederik P Kingma and Jimmy Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980(2014).Google Scholar
- Yehuda Koren. 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. ACM, 426-434. Google ScholarDigital Library
- Yehuda Koren, Robert Bell, and Chris Volinsky. 2009. Matrix factorization techniques for recommender systems. Computer42, 8 (2009). Google ScholarDigital Library
- Chenyi Lei, Dong Liu, Weiping Li, Zheng-Jun Zha, and Houqiang Li. 2016. Comparative deep learning of hybrid representations for image recommendations. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2545-2553.Google ScholarCross Ref
- Gai Li and Weihua Ou. 2016. Pairwise probabilistic matrix factorization for implicit feedback collaborative filtering. Neurocomputing204(2016), 17-25. Google ScholarDigital Library
- Daryl Lim, Julian McAuley, and Gert Lanckriet. 2015. Top-n recommendation with missing implicit feedback. In Proceedings of the 9th ACM Conference on Recommender Systems. ACM, 309-312. Google ScholarDigital Library
- Wei Niu, James Caverlee, and Haokai Lu. 2018. Neural Personalized Ranking for Image Recommendation. In Proceedings of 11th ACM International Conference on Web Search and Data Mining. Google ScholarDigital Library
- Rong Pan, Yunhong Zhou, Bin Cao, Nathan N Liu, Rajan Lukose, Martin Scholz, and Qiang Yang. 2008. One-class collaborative filtering. In Data Mining, 2008. ICDM'08. Eighth IEEE International Conference on. IEEE, 502-511. Google ScholarDigital Library
- Aghny Arisya Putra, Rahmad Mahendra, Indra Budi, and Qorib Munajat. 2017. Two-steps graph-based collaborative filtering using user and item similarities: Case study of E-commerce recommender systems. In Data and Software Engineering (ICoDSE), 2017 International Conference on. IEEE, 1-6.Google ScholarCross Ref
- Steffen Rendle, Christoph Freudenthaler, Zeno Gantner, and Lars Schmidt-Thieme. 2009. BPR: Bayesian personalized ranking from implicit feedback. In Proceedings of the twenty-fifth conference on uncertainty in artificial intelligence. AUAI Press, 452-461. Google ScholarDigital Library
- Ruslan Salakhutdinov, Andriy Mnih, and Geoffrey Hinton. 2007. Restricted Boltzmann machines for collaborative filtering. In Proceedings of the 24th international conference on Machine learning. ACM, 791-798. Google ScholarDigital Library
- Badrul Sarwar, George Karypis, Joseph Konstan, and John Riedl. 2001. Item-based collaborative filtering recommendation algorithms. In Proceedings of the 10th international conference on World Wide Web. ACM, 285-295. 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. ACM, 111-112. Google ScholarDigital Library
- Hao Wang, Naiyan Wang, and Dit-Yan Yeung. 2015. Collaborative deep learning for recommender systems. In Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 1235-1244. Google ScholarDigital Library
- Jun Wang, Arjen P De Vries, and Marcel JT Reinders. 2006. Unifying user-based and item-based collaborative filtering approaches by similarity fusion. In Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval. ACM, 501-508. Google ScholarDigital Library
- Jason Weston, Samy Bengio, and Nicolas Usunier. 2011. Wsabie: Scaling up to large vocabulary image annotation. In IJCAI, Vol. 11. 2764-2770. Google ScholarDigital Library
- Yao Wu, Christopher DuBois, Alice X Zheng, and Martin Ester. 2016. Collaborative denoising auto-encoders for top-n recommender systems. In Proceedings of the Ninth ACM International Conference on Web Search and Data Mining. ACM, 153-162. Google ScholarDigital Library
- Pratibha Yadav and Shweta Tyagi. 2017. Hybrid fuzzy collaborative filtering: an integration of item-based and user-based clustering techniques. International Journal of Computational Science and Engineering15, 3-4(2017), 295-310. Google ScholarDigital Library
- Akihiro Yamashita, Hidenori Kawamura, and Keiji Suzuki. 2011. Adaptive fusion method for user-based and item-based collaborative filtering. Advances in Complex Systems14, 02 (2011), 133-149.Google Scholar
- Shuang-Hong Yang, Bo Long, Alexander J Smola, Hongyuan Zha, and Zhaohui Zheng. 2011. Collaborative competitive filtering: learning recommender using context of user choice. In Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval. ACM, 295-304. Google ScholarDigital Library
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