Deep Social Collaborative Filtering

TL;DR

This paper introduces DSCF, a deep learning framework for social collaborative filtering that leverages both direct and distant social network information, considering neighbor opinions for improved recommendations.

Contribution

The paper proposes a novel deep social collaborative filtering model that effectively utilizes social network information, including distant neighbors and opinion relevance, for enhanced recommendations.

Findings

DSCF outperforms existing models on real-world datasets.

Incorporating distant social neighbors improves recommendation accuracy.

Explicit modeling of neighbor opinions enhances personalized recommendations.

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 filtering 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 influence. 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 specific recommendations. However, for a specific recommendation case, the information relevant to the specific item would be helpful. Besides, most of these models do not explicitly capture the neighbor's opinions to items for social recommendations, while different opinions could affect the user differently. 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 effectiveness of the proposed framework.