Recommendation with multi-source heterogeneous information

Li Gao, Hong Yang, Jia Wu, Chuan Zhou, Weixue Lu, Yue Hu

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contribution

12 Citations (Scopus)

Abstract

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.
Original languageEnglish
Title of host publicationProceedings of the 27th International Joint Conference on Artificial Intelligence
EditorsJérôme Lang
PublisherInternational Joint Conferences on Artificial Intelligence
Pages3378-3384
Number of pages7
ISBN (Electronic)9780999241127
DOIs
Publication statusPublished - 2018
Event27th International Joint Conference on Artificial Intelligence and the 23rd European Conference on Artificial Intelligence, IJCAI-ECAI 2018 - Stockholm, Sweden
Duration: 13 Jul 201819 Jul 2018

Conference

Conference27th International Joint Conference on Artificial Intelligence and the 23rd European Conference on Artificial Intelligence, IJCAI-ECAI 2018
CountrySweden
CityStockholm
Period13/07/1819/07/18

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  • Cite this

    Gao, L., Yang, H., Wu, J., Zhou, C., Lu, W., & Hu, Y. (2018). Recommendation with multi-source heterogeneous information. In J. Lang (Ed.), Proceedings of the 27th International Joint Conference on Artificial Intelligence (pp. 3378-3384). International Joint Conferences on Artificial Intelligence. https://doi.org/10.24963/ijcai.2018/469