Abstract
Recommender system has been well investigated in the past years. However, the typical representative CF-like models often give recommendation with low accuracy when the interaction information between users and items are sparse. To address the practical issue, in this paper we develop a novel Representation Learning with Depth and Breadth (RLDB) model for better recommendation Specifically, we design a heterogeneous network embedding method and convolutional neural network based method to learn feature representations of users and items from user-item interaction structure and review texts, respectively. Furthermore, an end-to-end breadth learning model is proposed through employing multi-view machine technique to learn features and fuse these diverse types of features in a uniform framework. Extensive experiments clearly demonstrates that our model outperforms all the other methods in these datasets.
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Acknowledgments
This work is supported by the National Natural Science Foundation of China (No. 61772082, 61375058), the National Key Research and Development Program of China (2017YFB0803304), and the Beijing Municipal Natural Science Foundation (4182043).
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Han, X., Shi, C., Zheng, L., Yu, P.S., Li, J., Lu, Y. (2018). Representation Learning with Depth and Breadth for Recommendation Using Multi-view Data. 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_15
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DOI: https://doi.org/10.1007/978-3-319-96890-2_15
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