{"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/rank-geofm-a-ranking-based-geographical","title":"Rank-GeoFM: A Ranking based Geographical Factorization Method for Point of Interest Recommendation","arxiv_id":null,"date":"2015-08-01","proceeding":null,"authors":["Xutao  Li","Gao Cong","Xiaoli  Li","Tuan Anh Nguyen Pham","Shonali Priyadarsini Krishnaswamy"],"abstract":"With the rapid growth of location-based social networks, Point of Interest (POI) recommendation has become an\r\nimportant research problem. However, the scarcity of the\r\ncheck-in data, a type of implicit feedback data, poses a severe challenge for existing POI recommendation methods. Moreover, different types of context information about\r\nPOIs are available and how to leverage them becomes another challenge. In this paper, we propose a ranking based\r\ngeographical factorization method, called Rank-GeoFM, for\r\nPOI recommendation, which addresses the two challenges.\r\nIn the proposed model, we consider that the check-in frequency characterizes users’ visiting preference and learn the\r\nfactorization by ranking the POIs correctly. In our model, POIs both with and without check-ins will contribute to\r\nlearning the ranking and thus the data sparsity problem can\r\nbe alleviated. In addition, our model can easily incorporate different types of context information, such as the geographical influence and temporal influence. We propose a\r\nstochastic gradient descent based algorithm to learn the factorization. Experiments on publicly available datasets under\r\nboth user-POI setting and user-time-POI setting have been\r\nconducted to test the effectiveness of the proposed method.\r\nExperimental results under both settings show that the proposed method outperforms the state-of-the-art methods significantly in terms of recommendation accuracy.","url_abs":"https://dl.acm.org/doi/10.1145/2766462.2767722","url_pdf":"https://dl.acm.org/doi/10.1145/2766462.2767722","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":"rank-geofm-a-ranking-based-geographical","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":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}