Exploiting dynamic social feedback for session-based recommendation

doi:10.1016/j.ipm.2023.103632

2024-05-01

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DOI：10.1016/j.ipm.2023.103632

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摘要

En 中文

Since people with close relationships are easily influenced by each other, social friends usually have more preferences of higher similarities than others. For this reason, social recommendation methods are proposed to adopt social links to improve the degree of preference matching between users and recommended items. Although current social recommendation methods have captured the general preference similarities among social friends, it is still difficult to model the evolution of dynamic social influence among friends, especially in session-based scenarios. In reality, when users' dynamic preferences are changing, social feedback from their friends is also changing over time. So that the dynamic social feedback is an important social influence, which has not been considered in current studies. To this end, we propose a social feedback-enhanced session-based recommendation (SFRec) method, which not only utilizes the similarity of general preferences among friends but also captures the friends' influence which reflects people's dynamic preferences. Specifically, we first coordinate similarity relations via information propagation on social graph, item transition graph and user-item interaction graph. To capture social feedback based on users' dynamic preferences, we then construct a social feedback generation module that consists of preference extraction, feedback generation and feedback aggregation. Finally, we construct a preference fusion module to obtain the final preference representation and make personalized recommendation. We conduct comprehensive experiments on three datasets. Results demonstrate that SFRec surpasses the state-of-the-art models on recommendation performance.

Keyword:

Social recommendation

Session-based recommendation

Graph neural network

Social feedback

Recommender systems