Abstract
Deep learning has been successfully introduced to collaborative filtering for recommendation systems in recent years. In these studies, autoencoder models are usually used to extract latent features of items or users, and related researches facilitate the learning techniques using item and user latent factors in matrix factorization models. Inputs of autoencoder models are usually side information of items or profile information of users. However, in many real world applications, we do not have users’ profile information. Moreover, results of both matrix factorization and deep learning models are difficult to interpret. To solve these issues, in this paper, we propose a deep collaborative filtering model with attention mechanism. With this model, learning of latent factors of users and items can be facilitated by deep processing of items’ tag information. Furthermore, user preferences learned are interpretable. Experiments conducted on a real world dataset demonstrate that our model can significantly outperform the state-of-the-art deep collaborative filtering models.
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Acknowledgment
This work was supported in part by National Natural Science Foundation of China under grant No. U1711262, 71771131, 71272029, 71490724 and 61472426.
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Li, F., Liu, H., He, J., Du, X. (2018). Attentive and Collaborative Deep Learning for Recommendation. In: Cai, Y., Ishikawa, Y., Xu, J. (eds) Web and Big Data. APWeb-WAIM 2018. Lecture Notes in Computer Science(), vol 10987. Springer, Cham. https://doi.org/10.1007/978-3-319-96890-2_16
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DOI: https://doi.org/10.1007/978-3-319-96890-2_16
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