SoGeM: Social Based Generative Model for Top-N Recommendation

Social recommendation which incorporates social information has attracted wide attention across both academia and industry for its superior performance. However, most existing approaches interpret social information in a heuristic manner which is not effective to capture the strong interplay between social connections and behaviors of users. This paper proposes SoGeM (SOcial based GEnerative Model) which simulates user's behaviors in a generative way and models intrinsic preferences and social influences of users simultaneously. Different from the most approaches that preassign similarity weights between friends, SoGeM learns the social influences automatically and quantitatively. Thus, the learnt influence has a probabilistic interpretation, for it is produced along with the generative process. We use Gibbs Sampling to train SoGeM and conduct comprehensive experiments on three real datasets. The results show that SoGeM outperforms other state-of-the-art approaches.

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