{"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/hybrid-collaborative-recommendation-via-semi","title":"Hybrid Collaborative Recommendation via Semi-AutoEncoder","arxiv_id":"1706.04453","date":"2017-08-16","proceeding":null,"authors":["Zhang Shuai","Yao Lina","Xu Xiwei","Wang Sen","Zhu Liming"],"abstract":"In this paper, we present a novel structure, Semi-AutoEncoder, based on\nAutoEncoder. We generalize it into a hybrid collaborative filtering model for\nrating prediction as well as personalized top-n recommendations. Experimental\nresults on two real-world datasets demonstrate its state-of-the-art\nperformances.","url_abs":"http://arxiv.org/abs/1706.04453v2","url_pdf":"http://arxiv.org/pdf/1706.04453v2.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":"hybrid-collaborative-recommendation-via-semi","repo_url":"https://github.com/cheungdaven/semi-ae-recsys","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"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}