Point-of-interest recommendation integrating user perception and multi-factor

Traditional collaborative filtering-based point-of-interest (POI) recommendation methods suffer from data sparsity and existing work tends to use contextual information alone without a reasonable balance of the effect of each factor. A point-of-interest recommendation model integrating user perception and multi-factor (UPMF) was proposed. User similarities for user perception-based implicit modelling were extracted to enrich user representations, and contextual information such as sequence, geography and social was used to construct POI recommendation models with synergistic user perception influences, which alleviated data sparsity. A novel user perception integration strategy (UPIS) was designed to mine the dynamic preferences of users based on user perceptions while making reasonable use of various contextual information. In addition, a segmentation-based activity area selection algorithm was proposed to model the impact of different activity areas on users. Results show that UPMF can improve the performance on metrics of precision, recall and normalized discounted cumulative gain (NDCG) compared to other popular POI recommendation technologies. Specifically, UPMF outperforms SUCP by 12.77% and 7.24% on the Gowalla and Yelp datasets in terms of NDCG@10, respectively. © 2023 Zhejiang University. All rights reserved.