{"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/transnets-learning-to-transform-for","title":"TransNets: Learning to Transform for Recommendation","arxiv_id":"1704.02298","date":"2017-04-07","proceeding":null,"authors":["Rose Catherine","William Cohen"],"abstract":"Recently, deep learning methods have been shown to improve the performance of\nrecommender systems over traditional methods, especially when review text is\navailable. For example, a recent model, DeepCoNN, uses neural nets to learn one\nlatent representation for the text of all reviews written by a target user, and\na second latent representation for the text of all reviews for a target item,\nand then combines these latent representations to obtain state-of-the-art\nperformance on recommendation tasks. We show that (unsurprisingly) much of the\npredictive value of review text comes from reviews of the target user for the\ntarget item. We then introduce a way in which this information can be used in\nrecommendation, even when the target user's review for the target item is not\navailable. Our model, called TransNets, extends the DeepCoNN model by\nintroducing an additional latent layer representing the target user-target item\npair. We then regularize this layer, at training time, to be similar to another\nlatent representation of the target user's review of the target item. We show\nthat TransNets and extensions of it improve substantially over the previous\nstate-of-the-art.","url_abs":"http://arxiv.org/abs/1704.02298v2","url_pdf":"http://arxiv.org/pdf/1704.02298v2.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":"transnets-learning-to-transform-for","repo_url":"https://github.com/noveens/reviews4rec","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"transnets-learning-to-transform-for","repo_url":"https://github.com/rosecatherinek/TransNets","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null}],"tasks":[{"task_slug":"recommendation-systems","task_name":"Recommendation Systems"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}