{"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/inferring-complementary-products-from-baskets","title":"Inferring Complementary Products from Baskets and Browsing Sessions","arxiv_id":"1809.09621","date":"2018-09-25","proceeding":null,"authors":["Ilya Trofimov"],"abstract":"Complementary products recommendation is an important problem in e-commerce.\nSuch recommendations increase the average order price and the number of\nproducts in baskets. Complementary products are typically inferred from basket\ndata. In this study, we propose the BB2vec model. The BB2vec model learns\nvector representations of products by analyzing jointly two types of data -\nBaskets and Browsing sessions (visiting web pages of products). These vector\nrepresentations are used for making complementary products recommendation. The\nproposed model alleviates the cold start problem by delivering better\nrecommendations for products having few or no purchases. We show that the\nBB2vec model has better performance than other models which use only basket\ndata.","url_abs":"http://arxiv.org/abs/1809.09621v1","url_pdf":"http://arxiv.org/pdf/1809.09621v1.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":"inferring-complementary-products-from-baskets","repo_url":"https://github.com/IlyaTrofimov/bb2vec","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}