Paper Info
Title
Time-semantic-aware Poisson tensor factorization approach for scalable hotel recommendation.
Abstract
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.
Year
DOI
Venue
2019
10.1016/j.ins.2019.07.068
Information Sciences
Keywords
Field
DocType
Hotel recommendation system,User reviews,Poisson factorization,Topic models,Multi-aspect ratings
Tourism,Factorization,Artificial intelligence,Tensor factorization,Poisson distribution,Machine learning,Mathematics,Scalability
Journal
Volume
ISSN
Citations 
504
0020-0255
2
PageRank 
References 
Authors
0.37
0
3
Name
Order
Citations
PageRank
Shang Liu191.14
Zhenzhong Chen21244101.41
Xiaolei Li3175.16