Abstract
Point-of-interest (POI) recommendations in location-based social networks (LBSNs) allow online users to discover various POIs for social activities occurring in the near future close to their current locations. Research has verified that people’s preferences regarding POIs are significantly affected by various internal and external contextual factors, which are therefore worth extensive study for POI recommendation. However, although psychological effects have also been demonstrated to be significantly correlated with an individual’s preferences, such effects have been largely ignored in previous studies on POI recommendation. For this paper, inspired by the famous memory theory in psychology, we were interested in whether memory-based preferences could be derived from users’ check-in data. Furthermore, we investigated how to incorporate these memory-based preferences into an effective POI recommendation scheme. Consequently, we refer to Ebbinghaus’s theory on memory, which describes the attenuation of an individual’s memory in the form of a forgetting curve over time. We first created a memory-based POI preference attenuation model and then adopted it to evaluate individuals’ check-ins. Next, we employed the memory-based values of check-ins to calculate the POI preference similarity between users in an LBSN. Finally, based on this memory-based preference similarity, we developed a novel POI recommendation method. We experimentally evaluated the proposed method on a real LBSN data set crawled from Foursquare. The results demonstrate that our method, which incorporates the proposed memory-based preference similarity for POI recommendation, significantly outperforms other methods. In addition, we found the best value of the parameter H in the memory-based preference model that optimizes the recommendation performance. This value of H implies that an individual’s memory usually has an effect on their daily travel choices for approximately 300 days.
Highlights
With the development of emerging technologies such as GPS, mobile communication and wireless networks, location-based social networks (LBSNs), such as Foursquare, Gowalla and Facebook, have been widely adopted worldwide
One of the advantages of such geographical services is that online users can discover points of interest (POIs) for social activities occurring in the near future close to their current locations
We propose a novel collaborative filtering model based on the proposed similarity for POI recommendation, which considers the characteristics of users’ memory-based preference attenuation
Summary
With the development of emerging technologies such as GPS, mobile communication and wireless networks, location-based social networks (LBSNs), such as Foursquare, Gowalla and Facebook, have been widely adopted worldwide. LBSNs combine social, localization and mobility functionalities, among others, to pinpoint and precisely understand locations through the mining and analysis of users’ location data. In contrast to traditional social networks, LBSNs display users’ geographical information and enrich the spatial and temporal characteristics of locations with various information drawn from users’ mobility data. Massive volumes of individual trajectory data are constantly being generated and are available to be extracted from LBSNs, thereby promoting research on real-time trajectory mining, user behaviour prediction, traffic analysis and location recommendation. The location-based services in LBSNs allow users to add and share locations such as restaurants, shopping malls or cinemas [1]. One of the advantages of such geographical services is that online users can discover points of interest (POIs) for social activities occurring in the near future close to their current locations
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