Temporal Social Graph Network Hashing for Efficient Recommendation

Hashing-based recommender systems that represent users and items as binary hash codes are recently proposed to significantly improve time and space efficiency. However, the highly developed social media presents two major challenges to hashing-based recommendation algorithms. First, the boundary between information producers and consumers becomes blurred, resulting in the rapid emergence of massive online content. Meanwhile, users' limited information consumption capacity inevitably causes further interaction sparsity. The inherent high sparsity of data leads to insufficient hash learning. Second, a considerable amount of online content becomes fast-moving consumer goods, such as short videos and news commentary, causing frequent changes in user interests and item popularity. To address the above problems, we propose a Temporal Social Graph Network Hashing (TSGNH) method for efficient recommendation, which generates binary hash codes of users and items through dynamic-adaptive aggregation on a constructed temporal social graph network. Specifically, we build a temporal social graph network to fully capture the social information widely existing in practical recommendation scenarios and propose a dynamic-adaptive aggregation method to capture long-term and short-term characters of users and items. Furthermore, different from the discrete optimization approaches used by existing hashing-based recommendation methods, we devise an end-to-end hashing learning approach that incorporates balanced and de-correlated constraints to learn compact and informative binary hash codes tailored for recommendation scenarios. Extensive experiments on three widely evaluated recommendation datasets demonstrate the superiority of the proposed method.