{"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/scalable-and-interpretable-product","title":"Scalable and interpretable product recommendations via overlapping co-clustering","arxiv_id":"1604.02071","date":"2016-04-07","proceeding":null,"authors":["Reinhard Heckel","Michail Vlachos","Thomas Parnell","Celestine Dünner"],"abstract":"We consider the problem of generating interpretable recommendations by\nidentifying overlapping co-clusters of clients and products, based only on\npositive or implicit feedback. Our approach is applicable on very large\ndatasets because it exhibits almost linear complexity in the input examples and\nthe number of co-clusters. We show, both on real industrial data and on\npublicly available datasets, that the recommendation accuracy of our algorithm\nis competitive to that of state-of-art matrix factorization techniques. In\naddition, our technique has the advantage of offering recommendations that are\ntextually and visually interpretable. Finally, we examine how to implement our\ntechnique efficiently on Graphical Processing Units (GPUs).","url_abs":"http://arxiv.org/abs/1604.02071v2","url_pdf":"http://arxiv.org/pdf/1604.02071v2.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":"scalable-and-interpretable-product","repo_url":"https://github.com/RajatPawar/ProductRoulette","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"clustering","task_name":"Clustering"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}