Abstract
SummaryVenue recommendation plays a vital role in helping the user discovers interesting locations. However, most of the existing studies have never considered the case of multiple people visiting a venue together, such as inviting friends to dinner, so they ignore the influence of group preference on making decisions. Furthermore, the participants' influence on the inviter's decision is also affected by participation time, venue category, and visiting sequence. In this article, we propose an adaptive group‐aware topic model (AGATM) to provide TOP‐K venue recommendations. The model considers the individual's personalized preferences and group preferences at different times to provide venue recommendations by simulating the process of making decisions for venues. We conduct extensive experiments on two real datasets to evaluate the performance of our proposed method. The experimental results show that our model outperforms existing benchmark algorithms.
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