@inproceedings{cec56da96cf84cd9b883cac2b57fb5da,
title = "Representation learning with depth and breadth for recommendation using multi-view data",
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.",
keywords = "Heterogeneous information network embedding, Multi-view machine, Rating prediction, Recommender system",
author = "Xiaotian Han and Chuan Shi and Lei Zheng and Yu, {Philip S.} and Jianxin Li and Yuanfu Lu",
year = "2018",
doi = "10.1007/978-3-319-96890-215",
language = "English",
isbn = "9783319968896",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer-Verlag London Ltd.",
pages = "181--188",
editor = "Jianliang Xu and Yoshiharu Ishikawa and Yi Cai",
booktitle = "Web and Big Data: Second International Joint Conference, APWeb-WAIM 2018",
address = "Germany",
note = "2nd Asia Pacific Web and Web-Age Information Management Joint Conference on Web and Big Data, APWeb-WAIM 2018 ; Conference date: 23-07-2018 Through 25-07-2018",
}