Recommendation with Multi-Source Heterogeneous Information
Recommendation with Multi-Source Heterogeneous Information
Li Gao, Hong Yang, Jia Wu, Chuan Zhou, Weixue Lu, Yue Hu
Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence
Main track. Pages 3378-3384.
https://doi.org/10.24963/ijcai.2018/469
Network embedding has been recently used in social network recommendations by embedding low-dimensional representations of network items for recommendation. However, existing item recommendation models in social networks suffer from two limitations. First, these models partially use item information and mostly ignore important contextual information in social networks such as textual content and social tag information. Second, network embedding and item recommendations are learned in two independent steps without any interaction. To this end, we in this paper consider item recommendations based on heterogeneous information sources. Specifically, we combine item structure, textual content and tag information for recommendation. To model the multi-source heterogeneous information, we use two coupled neural networks to capture the deep network representations of items, based on which a new recommendation model Collaborative multi-source Deep Network Embedding (CDNE for short) is proposed to learn different latent representations. Experimental results on two real-world data sets demonstrate that CDNE can use network representation learning to boost the recommendation performance.
Keywords:
Machine Learning: Machine Learning
Machine Learning: Recommender Systems