Abstract
The purchase decision of users is influenced by their basic preference of items, as well as the social influence of peers. Most social recommendation methods focus on incorporating the semantic collaborative information of social friends. In this paper, we argue that the semantic strength of their friends is also influenced by the subnetwork structure of friendship groups, which had not been well addressed in social recommendation literature. We propose a deep adversarial social model (SoGAN) that can automatically integrate the subnetwork structure of social groups and their semantic information into a unified recommendation framework. Specifically, we first align users in two different views, i.e., the “social-friend” view and “co-purchase” view. Then a generative adversarial network is used to learn the structure information of social groups to enhance the performance of recommender systems. We utilize the structural similarity between two views to produce true samples in SoGAN, and generate the mimic data based on the similarity between the semantic representations of users in two views. By discriminating the true instances based on structure similarity, we naturally inject the structure information into semantic learning of users. Extensive experiments on three real-world datasets, show the superiority of incorporating the social structure impact in recommender systems.
This work is supported by the National Natural Science Foundation of China under Grant No. 62102038; the National Natural Science Foundation of China under Grant No. 61972047, the NSFC-General Technology Basic Research Joint Funds under Grant U1936220.
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Bai, T., Huang, Y., Wu, B. (2022). Learning Social Influence from Network Structure for Recommender Systems. In: Bhattacharya, A., et al. Database Systems for Advanced Applications. DASFAA 2022. Lecture Notes in Computer Science, vol 13246. Springer, Cham. https://doi.org/10.1007/978-3-031-00126-0_7
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