{"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/learning-consumer-and-producer-embeddings-for","title":"Learning Consumer and Producer Embeddings for User-Generated Content Recommendation","arxiv_id":"1809.09739","date":"2018-09-25","proceeding":null,"authors":["Kang Wang-Cheng","McAuley Julian"],"abstract":"User-Generated Content (UGC) is at the core of web applications where users\ncan both produce and consume content. This differs from traditional e-Commerce\ndomains where content producers and consumers are usually from two separate\ngroups. In this work, we propose a method CPRec (consumer and producer based\nrecommendation), for recommending content on UGC-based platforms. Specifically,\nwe learn a core embedding for each user and two transformation matrices to\nproject the user's core embedding into two 'role' embeddings (i.e., a producer\nand consumer role). We model each interaction by the ternary relation between\nthe consumer, the consumed item, and its producer. Empirical studies on two\nlarge-scale UGC applications show that our method outperforms standard\ncollaborative filtering methods as well as recent methods that model producer\ninformation via item features.","url_abs":"http://arxiv.org/abs/1809.09739v1","url_pdf":"http://arxiv.org/pdf/1809.09739v1.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":"learning-consumer-and-producer-embeddings-for","repo_url":"https://github.com/kang205/CPRec","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"tf","reach":null}],"tasks":[{"task_slug":"collaborative-filtering","task_name":"Collaborative Filtering"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}