Bayesian dual neural networks for recommendation
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Most traditional collaborative filtering (CF) methods only use the user-item rating matrix to make recommendations, which usually suffer from cold-start and sparsity problems. To address these problems, on the one hand, some CF methods are proposed to incorporate auxiliary information such as user/item profiles; on the other hand, deep neural networks, which have powerful ability in learning effective representations, have achieved great success in recommender systems. However, these neural network based recommendation methods rarely consider the uncertainty of weights in the network and only obtain point estimates of the weights. Therefore, they maybe lack of calibrated probabilistic predictions and make overly confident decisions. To this end, we propose a new Bayesian dual neural network framework, named BDNet, to incorporate auxiliary information for recommendation. Specifically, we design two neural networks, one is to learn a common low dimensional space for users and items from the rating matrix, and another one is to project the attributes of users and items into another shared latent space. After that, the outputs of these two neural networks are combined to produce the final prediction. Furthermore, we introduce the uncertainty to all weights which are represented by probability distributions in our neural networks to make calibrated probabilistic predictions. Extensive experiments on real-world data sets are conducted to demonstrate the superiority of our model over various kinds of competitors.
Keywordscollaborative filtering Bayesian neural network hybrid recommendation algorithm
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The research work was supported by the National Key R&D Program of China (2018YFB1004300), the National Natural Science Foundation of China (Grant Nos. 61773361, 61473273, 91546122), the Science and Technology Project of Guangdong Province (2015B010109005), the Project of Youth Innovation Promotion Association CAS (2017146). This work was also partly supported by the funding of WeChat cooperation project. We thank Bo Chen, Leyu Lin, Cheng Niu, Xiaohu Cheng for their constructive advices.
- 1.Rendle S, Freudenthaler C, Gantner Z, Schmidt-Thieme L. BPR: Bayesian personalized ranking from implicit feedback. In: Proceedings of the 25th Conference on Uncertainty in Artificial Intelligence. 2009, 452–461Google Scholar
- 2.Salakhutdinov R, Mnih A Probabilistic matrix factorization. In: Proceedings of the 20th In ternational conference on Advances in Neural Information Processing Systems. 2007, 1257–1264Google Scholar
- 3.Xue H J, Dai X, Zhang J, Huang S, Chen J. Deep matrix factorization models for recommender systems. In: Proceedings of the 26th International Joint Conference on Artificial Intelligence. 2017, 3203–3209Google Scholar
- 4.Zhou Y, Wilkinson D, Schreiber R, Pan R. Large-scale parallel collaborative filtering for the netflix prize. In: Proceedings of the International Conference on Algorithmic Applications in Management. 2008, 337–348Google Scholar
- 6.Ma H, Zhou D, Liu C, Lyu M R, King I. Recommender systems with social regularization. In: Proceedings of the 4th ACM International Conference on Web Search and Data Mining. 2011, 287–296Google Scholar
- 7.Singh A, Gordon G J. A Bayesian matrix factorization model for relational data. In: Proceedings of the 26th Conference on Uncertainty in Artificial Intelligence. 2010, 556–563Google Scholar
- 8.Gao Y, Wang X, Lei G, Chen Z. Learning to recommend with collaborative matrix factorization for new users. Journal of Computer Research and Development, 2017, 54(8): 1813–1823Google Scholar
- 11.Li S, Kawale J, Fu Y. Deep collaborative filtering via marginalized denoising auto-encoder. In: Proceedings of the 24th ACM International on Conference on Information and Knowledge Management. 2015, 811–820Google Scholar
- 14.Wang X, Wang Y. Improving content-based and hybrid music recommendation using deep learning. In: Proceedings of the 22nd ACM International Conference on Multimedia. 2014, 627–636Google Scholar
- 15.Hernández-Lobato J M, Adams R. Probabilistic backpropagation for scalable learning of Bayesian neural networks. In: Proceedings of the International Conference on Machine Learning. 2015, 1861–1869Google Scholar
- 16.Blundell C, Cornebise J, Kavukcuoglu K, Wierstra D. Weight uncertainty in neural networks. In: Proceedings of the the 32nd International Conference on Machine Learning. 2015, 1613–1622Google Scholar
- 18.Lang K. Newsweeder: learning to filter netnews. In: Proceedings of the International Conference on Machine Learning. 1995, 331–339Google Scholar
- 22.Li W J, Yeung D Y, Zhang Z. Generalized latent factor models for social network analysis. In: Proceedings of the 22nd International Joint Conference on Artificial Intelligence. 2011, 1705–1710Google Scholar
- 23.Salakhutdinov R, Mnih A, Hinton G. Restricted boltzmann machines for collaborative filtering. In: Proceedings of the 24th International Conference on Machine learning. 2007, 791–798Google Scholar
- 24.Georgiev K, Nakov P. A non-IID framework for collaborative filtering with restricted boltzmann machines. In: Proceedings of the International Conference on Machine Learning. 2013, 1148–1156Google Scholar
- 25.Graves A. Practical variational inference for neural networks. In: Proceedings of the 24th International conference on Neural Information Processing Systems. 2011, 2348–2356Google Scholar
- 26.Salakhutdinov R, Mnih A. Bayesian probabilistic matrix factorization using markov chain monte carlo. In: Proceedings of the the 25th International Conference on Machine Learning. 2008, 880–887Google Scholar