Time-semantic-aware Poisson tensor factorization approach for scalable hotel recommendation.

With the rapid development of tourism, personalized hotel recommendation has drawn growing attention from researchers, as online hotel booking becomes a challenging task when users confronted with a large volume of hotel information. However, most of conventional rating-based hotel recommendation approaches do not explicitly take into account the temporal behavior and review texts of users, which are essential to recommend hotels that match users’ preferences. In this paper, we propose a time-semantic-aware Poisson tensor factorization approach to learn comprehensively the temporal dynamics, multi-aspect ratings and review texts for hotel recommendation. This approach captures periodic effects of user activity with a time-aware factorization model. By exploiting the shared latent structure between users’ multi-aspect ratings and review texts, the approach has been shown to be able to predict more precisely the user’s preferences on hotels and easily be generalized to cold-start users for scalable hotel recommendation. To evaluate the performance of proposed method, experiments are conducted on two datasets collected from TripAdvisor. Experimental results of top-k hotel recommendation task reveal that the proposed model outperforms the state-of-the-art recommendation methods with a significant margin.