Point-of-interest recommendation in location-based social networks based on collaborative filtering and spatial kernel weighting

The growing popularity of location-based social networks (LBSNs), as well as their financial benefits, has led to a variety of methods for point-of-interest (POI) recommendations. A review of various methodologies reveals the role of geography is rarely addressed individually in current procedures. Instead, it is treated as an overall probability distribution of distances between all check-in pairs. On the other hand, despite the increased accuracy, the incorporation of individualized probabilistic distributions complicates matters of recommender systems. To address this issue, the main objective of this study is to introduce a POI recommendation mechanism in LBSNs that could be simple to implement the geographical influence. Furthermore, the method could be easily integrated with collaborative filtering while also increasing the quality of recommendations. Hence, instead of establishing a unique probability function for all check-ins data using the power-law or the Gaussian distribution, a spatial kernel weighting technique inspired by the similarity-weighted average notion, called CFSKW, was introduced and implemented. CFSKW was found on the principle that POIs close to a person's favourite POIs are more likely to be visited. As expected, the proposed method outperformed state-of-the-arts when tested utilizing two Four square datasets, namely New York and Tokyo. Kernel bandwidth and the coefficient of integrating user preference with geographical influence were two critical factors to be tuned. Low bandwidth and a medium to high geographical coefficient, according to the findings, produce better results. In unproportionate density of POIs, like that of the Tokyo dataset, a proposed algorithm based on dynamic bandwidth showed promising results. For the New York dataset, Precision@5 and Recall@5 were obtained as 25.41% and 61.80% using the proposed method, respectively, indicating a 3% and 5.1% improvement over the reference techniques. Precision@5 and Recall@5 were 1.1% and 1.2% better for the Tokyo dataset, respectively, when using a dynamic bandwidth geographical similarity algorithm.