POI Recommendation Algorithm based on Region Transfer Collaborative Filtering
Kang Liu, Wenguang Zheng, Yingyuan Xiao, Xingyu Zhai · 2022 IEEE 25th International Conference on Computer Supported Cooperative Work in Design (CSCWD) · 2022
With the development of mobile internet and social platforms, the lifestyle of check-in has become popular in people’s daily life. With the support of highly accurate positioning technology, the social platform has accumulated a large amount of user check-in data. Based on these data, the platform can provide point-of-initerest recommendation services for users.However, existing recommendation algorithms often suffer from some limitations: (1) Recommendation systems using deep learning algorithms require extremely high hardware computing power and can not be deployed on edge devices; (2) General recommendation algorithms do not fully exploit the potential of users to go to new points of interest when making recommendations. Based on the above existing problems, we propose our own collaborative filtering recommendation model: (1) The model contains simple operations and requires very little device computing power; (2) It models users’ activity areas by using a Gaussian distribution model, and then calculates the similarity between users to establish similar user groups. The similarity can be used as the probability of regional transfer of group members. Based on this method, the points-of-interest of similar users are added to the recommendation list for the purpose of exploring new interest points. The experimental results on the well-known dataset Gowalla demonstrate that the proposed approach outperforms other state-of-the-art POI recommendation methods.