{"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/from-word-embeddings-to-item-recommendation","title":"From Word Embeddings to Item Recommendation","arxiv_id":"1601.01356","date":"2016-01-07","proceeding":null,"authors":["Makbule Gulcin Ozsoy"],"abstract":"Social network platforms can use the data produced by their users to serve\nthem better. One of the services these platforms provide is recommendation\nservice. Recommendation systems can predict the future preferences of users\nusing their past preferences. In the recommendation systems literature there\nare various techniques, such as neighborhood based methods, machine-learning\nbased methods and matrix-factorization based methods. In this work, a set of\nwell known methods from natural language processing domain, namely Word2Vec, is\napplied to recommendation systems domain. Unlike previous works that use\nWord2Vec for recommendation, this work uses non-textual features, the\ncheck-ins, and it recommends venues to visit/check-in to the target users. For\nthe experiments, a Foursquare check-in dataset is used. The results show that\nuse of continuous vector space representations of items modeled by techniques\nof Word2Vec is promising for making recommendations.","url_abs":"http://arxiv.org/abs/1601.01356v3","url_pdf":"http://arxiv.org/pdf/1601.01356v3.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":"from-word-embeddings-to-item-recommendation","repo_url":"https://github.com/caroline171/content_based_recommendation","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"from-word-embeddings-to-item-recommendation","repo_url":"https://github.com/mgulcin/DL_Rec","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"recommendation-systems","task_name":"Recommendation Systems"},{"task_slug":"word-embeddings","task_name":"Word Embeddings"}],"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}