User-location distribution serves as a useful feature in item-based collaborative filtering

• Published in: Physica A 2022-01, Vol.586, p.126491, Article 126491
• Main Authors: Jiang, Liang-Chao, Liu, Run-Ran, Jia, Chun-Xiao
• Format: Article
• Language: English
• Subjects: Collaborative filtering; Diversity; User tastes; User-location distribution
• ISSN: 0378-4371; 1873-2119
• DOI: 10.1016/j.physa.2021.126491

Personalized recommender system is a powerful method to solve the problem of information overload, which has been widely applied in a variety of scenarios, such as e-commerce, video platforms and social networks, to help users find relevant items or friends of interest. Collaborative filtering is the most successful and widely used algorithm in the recommender systems as its powerful capability of generating recommendations by sharing collective experiences of users. In recent years, the use of mobile devices and the rapid development of internet infrastructures provide the possibility to analyze regional features of items based on user locations. Here we improve the performance of collaborative filtering by using user-location distribution to uncover the potential similarities between items. We find that the similarity of user-location distribution is one efficient measure for the item–item similarities in the framework of collaborative filtering to generate personalized recommendation for users. Furthermore, we have also mixed similarity measures of user-location distribution and the traditional method based on the number of common users linearly to optimize the performance of collaborative filtering. Based on the Movielens data set, we show that the performance of our methods could be improved in terms of the metrics of accuracy and diversity simultaneously. •A new measure of similarity for user-location distribution is proposed.•The proposed measure improves the quality of collaborative filtering greatly.•The proposed measure can be mixed with the traditional similarity measure.•The hybrid method improves the algorithmic accuracy and diversity simultaneously.