{"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/point-of-interest-recommendation-exploiting","title":"Point-of-Interest Recommendation: Exploiting Self-Attentive Autoencoders with Neighbor-Aware Influence","arxiv_id":"1809.10770","date":"2018-09-27","proceeding":null,"authors":["Chen Ma","Yingxue Zhang","Qinglong Wang","Xue Liu"],"abstract":"The rapid growth of Location-based Social Networks (LBSNs) provides a great\nopportunity to satisfy the strong demand for personalized Point-of-Interest\n(POI) recommendation services. However, with the tremendous increase of users\nand POIs, POI recommender systems still face several challenging problems: (1)\nthe hardness of modeling non-linear user-POI interactions from implicit\nfeedback; (2) the difficulty of incorporating context information such as POIs'\ngeographical coordinates. To cope with these challenges, we propose a novel\nautoencoder-based model to learn the non-linear user-POI relations, namely\n\\textit{SAE-NAD}, which consists of a self-attentive encoder (SAE) and a\nneighbor-aware decoder (NAD). In particular, unlike previous works equally\ntreat users' checked-in POIs, our self-attentive encoder adaptively\ndifferentiates the user preference degrees in multiple aspects, by adopting a\nmulti-dimensional attention mechanism. To incorporate the geographical context\ninformation, we propose a neighbor-aware decoder to make users' reachability\nhigher on the similar and nearby neighbors of checked-in POIs, which is\nachieved by the inner product of POI embeddings together with the radial basis\nfunction (RBF) kernel. To evaluate the proposed model, we conduct extensive\nexperiments on three real-world datasets with many state-of-the-art baseline\nmethods and evaluation metrics. The experimental results demonstrate the\neffectiveness of our model.","url_abs":"http://arxiv.org/abs/1809.10770v1","url_pdf":"http://arxiv.org/pdf/1809.10770v1.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":"point-of-interest-recommendation-exploiting","repo_url":"https://github.com/allenjack/SAE-NAD","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"decoder","task_name":"Decoder"},{"task_slug":"recommendation-systems","task_name":"Recommendation Systems"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1809.10770","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}