Location‐based social network recommendations with computational intelligence‐based similarity computation and user check‐in behavior. (24th November 2020)

• Record Type: Journal Article
• Authors: Elangovan, Rajalakshmi; Vairavasundaram, Subramaniyaswamy; Varadarajan, Vijayakumar; Ravi, Logesh
• Other Names: Jeon Gwanggil guestEditor.; Chehri Abdellah guestEditor.; Lu Huimin guestEditor.; Guna Jože guestEditor.
• Is Part Of: Concurrency and computation. Volume 33:Number 22(2021)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2020-11-24
• Subjects: activity features -- computational intelligence -- location‐based social communities -- point‐of‐interest -- recommender system -- similarity features -- spatial features; Parallel processing (Electronic computers) -- Periodicals; Parallel computers -- Periodicals; 004.35
• DOI: 10.1002/cpe.6106
• Languages: English
• ISSNs: 1532-0626

Abstract

Abstract: Location recommending frameworks plays a very significant role in suggesting the users with new places to visit especially when users are visiting unfamiliar areas. Most of the existing recommender systems do not consider the fact that different users have different behavior while checking in. Some approaches do not consider the essential factors while providing recommendations. These systems lack adaptability and hence they provide poor recommendations. An adaptive approach to provide users with a personalized recommendation has been proposed in this paper. We have considered three features namely, user activeness feature, user similarity feature, and the spatial feature. In addition to this, we have also considered the location popularity for a given timeslot. We have divided the users into inactive and active based on the degree of activeness on social networks using fuzzy c‐means clustering. We have provided two strategies based on the activeness of the user. A two‐dimensional Gaussian kernel density estimation strategy is used for the active user. A one‐dimensional power‐law function strategy is used for inactive users. Moreover, we have integrated the time‐based popularity of the location and probability estimation based on the similarity between the users. To evaluate the proposed model, we have used a large‐scale Foursquare dataset.