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We first describe the basic CF-NADE model for CF\ntasks. Then we propose to improve the model by sharing parameters between\ndifferent ratings. A factored version of CF-NADE is also proposed for better\nscalability. Furthermore, we take the ordinal nature of the preferences into\nconsideration and propose an ordinal cost to optimize CF-NADE, which shows\nsuperior performance. Finally, CF-NADE can be extended to a deep model, with\nonly moderately increased computational complexity. Experimental results show\nthat CF-NADE with a single hidden layer beats all previous state-of-the-art\nmethods on MovieLens 1M, MovieLens 10M, and Netflix datasets, and adding more\nhidden layers can further improve the performance.","url_abs":"http://arxiv.org/abs/1605.09477v1","url_pdf":"http://arxiv.org/pdf/1605.09477v1.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":"a-neural-autoregressive-approach-to","repo_url":"https://github.com/Build-Week-Spotify-Song-Suggester-5/Data-Science","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"a-neural-autoregressive-approach-to","repo_url":"https://github.com/JoonyoungYi/CFNADE-keras","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"a-neural-autoregressive-approach-to","repo_url":"https://github.com/dsanno/chainer-cf-nade","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok"}}],"tasks":[{"task_slug":"collaborative-filtering","task_name":"Collaborative Filtering"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/collaborative-filtering-on-movielens-10m","task":"Recommendation Systems","dataset":"MovieLens 10M","model":"CF-NADE","rank_in_archive_order":6,"of":17,"metrics":{"RMSE":"0.771"},"uses_additional_data":false},{"leaderboard":"/sota/collaborative-filtering-on-movielens-1m","task":"Recommendation Systems","dataset":"MovieLens 1M","model":"CF-NADE","rank_in_archive_order":3,"of":31,"metrics":{"RMSE":"0.829"},"uses_additional_data":false}],"syntology":{"syntology_url":null,"atlas_url":"https://app.syntology.ai/?focus=1605.09477","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1605.09477"}},"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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