{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/location-recommendation-in-location-based","title":"Location Recommendation in Location-based Social Networks using User Check-in Data","arxiv_id":null,"date":"2013-11-01","proceeding":null,"authors":["Hao Wang","Manolis Terrovitis","Nikos Mamoulis"],"abstract":"This paper studies the problem of recommending new venues\r\nto users who participate in location-based social networks\r\n(LBSNs). As an increasingly larger number of users partake\r\nin LBSNs, the recommendation problem in this setting\r\nhas attracted significant attention in research and in practical\r\napplications. The detailed information about past user\r\nbehavior that is traced by the LBSN differentiates the problem\r\nsignificantly from its traditional settings. The spatial\r\nnature in the past user behavior and also the information\r\nabout the user social interaction with other users, provide a\r\nricher background to build a more accurate and expressive\r\nrecommendation model.\r\nAlthough there have been extensive studies on recommender\r\nsystems working with user-item ratings, GPS trajectories,\r\nand other types of data, there are very few approaches\r\nthat exploit the unique properties of the LBSN user check-in\r\ndata. In this paper, we propose algorithms that create recommendations\r\nbased on four factors: a) past user behavior\r\n(visited places), b) the location of each venue, c) the social\r\nrelationships among the users, and d) the similarity between\r\nusers. The proposed algorithms outperform traditional recommendation\r\nalgorithms and other approaches that try to\r\nexploit LBSN information.\r\nTo design our recommendation algorithms we study the\r\nproperties of two real LBSNs, Brightkite and Gowalla, and\r\nanalyze the relation between users and visited locations. An\r\nexperimental evaluation using data from these LBSNs shows\r\nthat the exploitation of the additional geographical and social\r\ninformation allows our proposed","url_abs":"https://dl.acm.org/doi/10.1145/2525314.2525357","url_pdf":"https://dl.acm.org/doi/10.1145/2525314.2525357","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"location-recommendation-in-location-based","repo_url":"https://github.com/dbgroup-uestc/cuiyue","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"recommendation-systems","task_name":"Recommendation Systems"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}