{"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/joint-deep-modeling-of-users-and-items-using","title":"Joint Deep Modeling of Users and Items Using Reviews for Recommendation","arxiv_id":"1701.04783","date":"2017-01-17","proceeding":null,"authors":["Lei Zheng","Vahid Noroozi","Philip S. Yu"],"abstract":"A large amount of information exists in reviews written by users. This source\nof information has been ignored by most of the current recommender systems\nwhile it can potentially alleviate the sparsity problem and improve the quality\nof recommendations. In this paper, we present a deep model to learn item\nproperties and user behaviors jointly from review text. The proposed model,\nnamed Deep Cooperative Neural Networks (DeepCoNN), consists of two parallel\nneural networks coupled in the last layers. One of the networks focuses on\nlearning user behaviors exploiting reviews written by the user, and the other\none learns item properties from the reviews written for the item. A shared\nlayer is introduced on the top to couple these two networks together. The\nshared layer enables latent factors learned for users and items to interact\nwith each other in a manner similar to factorization machine techniques.\nExperimental results demonstrate that DeepCoNN significantly outperforms all\nbaseline recommender systems on a variety of datasets.","url_abs":"http://arxiv.org/abs/1701.04783v1","url_pdf":"http://arxiv.org/pdf/1701.04783v1.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":"joint-deep-modeling-of-users-and-items-using","repo_url":"https://github.com/HuijunZhao/recommendation-system","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok"}},{"paper_slug":"joint-deep-modeling-of-users-and-items-using","repo_url":"https://github.com/TianHongTao/-Recommendation-Improved","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}},{"paper_slug":"joint-deep-modeling-of-users-and-items-using","repo_url":"https://github.com/TianHongTao/ID-DAML","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"joint-deep-modeling-of-users-and-items-using","repo_url":"https://github.com/TianHongTao/Recommendation-System-Graduation-Design","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}},{"paper_slug":"joint-deep-modeling-of-users-and-items-using","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"}}],"tasks":[{"task_slug":"recommendation-systems","task_name":"Recommendation Systems"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1701.04783","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1701.04783"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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