DF-LR: A Lightweight POI Recommendation Method Integrating Mobile Direction Awareness and Filtering Mechanisms

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Point-of-interest (POI) recommendation can provide personalized and intelligent location recommendation services, which is of significant value in location-based social networks. However, current research lacks comprehensive awareness of user mobile contexts, and POI recommendation algorithms perform calculations over all check-in data, resulting in high computational complexity and low accuracy. To address these issues, we propose a lightweight POI recommendation method called DF-LR, which combines mobile direction awareness and filtering mechanisms. Specifically, for mobile context awareness, we obtain the user’s location via GPS and the movement direction via the compass sensor in smartphones, then reduce noise using the median filtering method and propose a direction similarity calculation method. Additionally, we devise two filtering mechanisms to achieve lightweighting: POI filtering is performed by mining the distance features of user’s adjacent visit positions, and check-in data filtering is performed by mining the features of users’ check-ins at the same position. Finally, Jaccard-based user similarity and movement direction correlation are combined to calculate recommendation probability. Extensive performance evaluation experiments show that DF-LR improves precision, recall and F1-measure with lower computation overhead compared to baseline POI recommendations.