A Neural Influence Diffusion Model for Social Recommendation

Abstract

Precise user and item embedding learning is the key to building a successful recommender system. Traditionally, Collaborative Filtering (CF) provides a way to learn user and item embeddings from the user-item interaction history. However, the performance is limited due to the sparseness of user behavior data. With the emergence of online social networks, social recommender systems have been proposed to utilize each user's local neighbors' preferences to alleviate the data sparsity for better user embedding modeling. We argue that, for each user of a social platform, her potential embedding is influenced by her trusted users, with these trusted users are influenced by the trusted users' social connections. As social influence recursively propagates and diffuses in the social network, each user's interests change in the recursive process. Nevertheless, the current social recommendation models simply developed static models by leveraging the local neighbors of each user without simulating the recursive diffusion in the global social network, leading to suboptimal recommendation performance. In this paper, we propose a deep influence propagation model to stimulate how users are influenced by the recursive social diffusion process for social recommendation. For each user, the diffusion process starts with an initial embedding that fuses the related features and a free user latent vector that captures the latent behavior preference. The key idea of our proposed model is that we design a layer-wise influence propagation structure to model how users' latent embeddings evolve as the social diffusion process continues. We further show that our proposed model is general and could be applied when the user (item) attributes or the social network structure is not available. Finally, extensive experimental results on two real-world datasets clearly show the effectiveness of our proposed model, with more than 13% performance improvements over the best baselines for top-10 recommendation on the two datasets.

Meeting Name

42nd International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2019 (2019: Jul. 21-25, Paris, France)

Department(s)

Computer Science

Comments

This work was supported in part by grants from the National Natural Science Foundation of China (Grant No. 61725203, 61722204, 61602147, 61732008, 61632007), the Anhui Provincial Natural Science Foundation (Grant No. 1708085QF155), and the Fundamental Research Funds for the Central Universities (Grant No. JZ2018HGTB0230).

Keywords and Phrases

Graph neural networks; Influence diffusion; Personalization; Social recommendation

International Standard Book Number (ISBN)

978-145036172-9

Document Type

Article - Conference proceedings

Document Version

Citation

File Type

text

Language(s)

English

Rights

© 2019 Association for Computing Machinery (ACM), All rights reserved.

Publication Date

01 Jul 2019

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