{"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/learning-points-and-routes-to-recommend","title":"Learning Points and Routes to Recommend Trajectories","arxiv_id":"1608.07051","date":"2016-08-25","proceeding":null,"authors":["Dawei Chen","Cheng Soon Ong","Lexing Xie"],"abstract":"The problem of recommending tours to travellers is an important and broadly\nstudied area. Suggested solutions include various approaches of\npoints-of-interest (POI) recommendation and route planning. We consider the\ntask of recommending a sequence of POIs, that simultaneously uses information\nabout POIs and routes. Our approach unifies the treatment of various sources of\ninformation by representing them as features in machine learning algorithms,\nenabling us to learn from past behaviour. Information about POIs are used to\nlearn a POI ranking model that accounts for the start and end points of tours.\nData about previous trajectories are used for learning transition patterns\nbetween POIs that enable us to recommend probable routes. In addition, a\nprobabilistic model is proposed to combine the results of POI ranking and the\nPOI to POI transitions. We propose a new F$_1$ score on pairs of POIs that\ncapture the order of visits. Empirical results show that our approach improves\non recent methods, and demonstrate that combining points and routes enables\nbetter trajectory recommendations.","url_abs":"http://arxiv.org/abs/1608.07051v1","url_pdf":"http://arxiv.org/pdf/1608.07051v1.pdf","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":"learning-points-and-routes-to-recommend","repo_url":"https://bitbucket.org/d-chen/tour-cikm16","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/1608.07051","atlas_url":"https://app.syntology.ai/?focus=1608.07051","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}