Deep Social Collaborative Filtering

Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45)32_Refereed conference paper (with ISBN/ISSN)peer-review

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Detail(s)

Original languageEnglish
Title of host publicationProceedings of the 13th ACM Conference on Recommender Systems
PublisherACM New York
Pages305-313
ISBN (Electronic)9781450362436
ISBN (Print)9781450362436
Publication statusPublished - 10 Sep 2019

Publication series

NameRecSys - ACM Conference on Recommender Systems

Conference

Title13th ACM Conference on Recommender Systems, RecSys 2019
PlaceDenmark
CityCopenhagen
Period16 - 20 September 2019

Abstract

Recommender systems are crucial to alleviate the information overload problem in online worlds. Most of the modern recommender systems capture users' preference towards items via their interactions based on collaborative fltering techniques. In addition to the user-item interactions, social networks can also provide useful information to understand users' preference as suggested by the social theories such as homophily and infuence. Recently, deep neural networks have been utilized for social recommendations, which facilitate both the user-item interactions and the social network information. However, most of these models cannot take full advantage of the social network information. They only use information from direct neighbors, but distant neighbors can also provide helpful information. Meanwhile, most of these models treat neighbors' information equally without considering the specifc recommendations. However, for a specifc recommendation case, the information relevant to the specifc item would be helpful. Besides, most of these models do not explicitly capture the neighbor's opinions to items for social recommendations, while diferent opinions could afect the user diferently. In this paper, to address the aforementioned challenges, we propose DSCF, a Deep Social Collaborative Filtering framework, which can exploit the social relations with various aspects for recommender systems. Comprehensive experiments on two-real world datasets show the efectiveness of the proposed framework.

Research Area(s)

  • Neural Networks, Random Walk, Recommender Systems, Recurrent Neural Network, Social Network, Social Recommendation

Citation Format(s)

Deep Social Collaborative Filtering. / Fan, Wenqi; Wang, Jianping; Ma, Yao et al.

Proceedings of the 13th ACM Conference on Recommender Systems. ACM New York, 2019. p. 305-313 (RecSys - ACM Conference on Recommender Systems).

Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45)32_Refereed conference paper (with ISBN/ISSN)peer-review