{"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/item2vec-neural-item-embedding-for","title":"Item2Vec: Neural Item Embedding for Collaborative Filtering","arxiv_id":"1603.04259","date":"2016-03-14","proceeding":null,"authors":["Oren Barkan","Noam Koenigstein"],"abstract":"Many Collaborative Filtering (CF) algorithms are item-based in the sense that\nthey analyze item-item relations in order to produce item similarities.\nRecently, several works in the field of Natural Language Processing (NLP)\nsuggested to learn a latent representation of words using neural embedding\nalgorithms. Among them, the Skip-gram with Negative Sampling (SGNS), also known\nas word2vec, was shown to provide state-of-the-art results on various\nlinguistics tasks. In this paper, we show that item-based CF can be cast in the\nsame framework of neural word embedding. Inspired by SGNS, we describe a method\nwe name item2vec for item-based CF that produces embedding for items in a\nlatent space. The method is capable of inferring item-item relations even when\nuser information is not available. We present experimental results that\ndemonstrate the effectiveness of the item2vec method and show it is competitive\nwith SVD.","url_abs":"http://arxiv.org/abs/1603.04259v3","url_pdf":"http://arxiv.org/pdf/1603.04259v3.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":"item2vec-neural-item-embedding-for","repo_url":"https://github.com/hyunbool/Frequent-Pattern-Mining-using-Language-Model","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok"}},{"paper_slug":"item2vec-neural-item-embedding-for","repo_url":"https://github.com/hyunbool/frequent_itemset_mining_using_language_model","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok"}},{"paper_slug":"item2vec-neural-item-embedding-for","repo_url":"https://github.com/hyunbool/item2vec_movie_practice","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok"}},{"paper_slug":"item2vec-neural-item-embedding-for","repo_url":"https://github.com/hyunbool/item_recommender_with_nlp","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok"}},{"paper_slug":"item2vec-neural-item-embedding-for","repo_url":"https://github.com/massquantity/LibRecommender","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"item2vec-neural-item-embedding-for","repo_url":"https://github.com/nnkkmto/item2vec-food-rec","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok"}},{"paper_slug":"item2vec-neural-item-embedding-for","repo_url":"https://github.com/nnkkmto/item2vec-rec-test","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok"}}],"tasks":[{"task_slug":"collaborative-filtering","task_name":"Collaborative Filtering"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1603.04259","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}