Trust-Enhanced Collaborative Filtering for Personalized Point of Interests Recommendation

Predicting the user's trajectory behavior sequence based on point of interests (POIs) recommendation is of great significance in the realization of the smart city with the emerging of social Internet of Things technology. One of the widely adopted frameworks is the user-based collaborative filtering, where the explicit POI rating is calculated based on similar users' preference. However, the trust between users is seldom considered. We believe that if two users show similar preferences or personality traits, the trust level between them should be high. To this end, we propose to calculate the trust-enhanced user similarity in user-based collaborative filtering based on network representation learning. Meanwhile, due to the significance of geographic influence and temporal influence, we integrate these two factors into POI recommendation by a fusion model. Therefore, our proposed POI recommendation system is unified collaborative recommendation framework, which fuses trust-enhanced users' preferences to potential POIs with geographic influences and temporal influence for POI recommendation. Finally, we conduct extensive experiments on two real-world datasets by comparing with several state-of-the-art methods in terms of precision@k and recall@k. Experimental results indicate that our proposed trust-enhanced collaborative filtering method outperforms other recommendation approaches.