{"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/dropoutnet-addressing-cold-start-in","title":"DropoutNet: Addressing Cold Start in Recommender Systems","arxiv_id":null,"date":"2017-12-01","proceeding":"NeurIPS 2017 12","authors":["Maksims Volkovs","Guangwei Yu","Tomi Poutanen"],"abstract":"Latent models have become the default choice for recommender systems due to their performance and scalability. However, research in this area has primarily focused on modeling user-item interactions,  and few latent models have been developed for cold start. Deep learning has recently achieved remarkable success showing excellent results for diverse input types. Inspired by these results we propose a neural network based latent model called DropoutNet to address the cold start problem in recommender systems. Unlike existing approaches that incorporate additional content-based objective terms, we instead focus on the optimization and show that neural network models can be explicitly trained for cold start through dropout. Our model can  be applied on top of any existing latent model effectively providing cold start capabilities, and full power of deep architectures. Empirically we demonstrate  state-of-the-art accuracy on publicly available benchmarks. Code is available at  https://github.com/layer6ai-labs/DropoutNet.","url_abs":"http://papers.nips.cc/paper/7081-dropoutnet-addressing-cold-start-in-recommender-systems","url_pdf":"http://papers.nips.cc/paper/7081-dropoutnet-addressing-cold-start-in-recommender-systems.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":"dropoutnet-addressing-cold-start-in","repo_url":"https://github.com/layer6ai-labs/DropoutNet","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"tf","reach":null},{"paper_slug":"dropoutnet-addressing-cold-start-in","repo_url":"https://github.com/alibaba/EasyRec/blob/master/easy_rec/python/model/dropoutnet.py","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"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}