{"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/training-deep-autoencoders-for-collaborative","title":"Training Deep AutoEncoders for Collaborative Filtering","arxiv_id":"1708.01715","date":"2017-08-05","proceeding":null,"authors":["Oleksii Kuchaiev","Boris Ginsburg"],"abstract":"This paper proposes a novel model for the rating prediction task in\nrecommender systems which significantly outperforms previous state-of-the art\nmodels on a time-split Netflix data set. Our model is based on deep autoencoder\nwith 6 layers and is trained end-to-end without any layer-wise pre-training. We\nempirically demonstrate that: a) deep autoencoder models generalize much better\nthan the shallow ones, b) non-linear activation functions with negative parts\nare crucial for training deep models, and c) heavy use of regularization\ntechniques such as dropout is necessary to prevent over-fiting. We also propose\na new training algorithm based on iterative output re-feeding to overcome\nnatural sparseness of collaborate filtering. The new algorithm significantly\nspeeds up training and improves model performance. Our code is available at\nhttps://github.com/NVIDIA/DeepRecommender","url_abs":"http://arxiv.org/abs/1708.01715v3","url_pdf":"http://arxiv.org/pdf/1708.01715v3.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":"training-deep-autoencoders-for-collaborative","repo_url":"https://github.com/NVIDIA/DeepRecommender","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"training-deep-autoencoders-for-collaborative","repo_url":"https://github.com/Chinmayrane16/DeepRecommender","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"training-deep-autoencoders-for-collaborative","repo_url":"https://github.com/JerryKwon/melon_plylst_continuation","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok"}},{"paper_slug":"training-deep-autoencoders-for-collaborative","repo_url":"https://github.com/butroy/movie-autoencoder","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null},{"paper_slug":"training-deep-autoencoders-for-collaborative","repo_url":"https://github.com/marlesson/recsys_autoencoders","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null},{"paper_slug":"training-deep-autoencoders-for-collaborative","repo_url":"https://github.com/suvigyavijay/DeepRecommender","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null},{"paper_slug":"training-deep-autoencoders-for-collaborative","repo_url":"https://github.com/yrbahn/Deep-AutoEncoders-for-Collaborative-Filtering","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null},{"paper_slug":"training-deep-autoencoders-for-collaborative","repo_url":"https://github.com/2023-MindSpore-1/ms-code-220/tree/main/DIEN","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"mindspore","reach":null},{"paper_slug":"training-deep-autoencoders-for-collaborative","repo_url":"https://github.com/PaddlePaddle/PaddleRec/tree/master/models/rank/deeprec","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"paddle","reach":null},{"paper_slug":"training-deep-autoencoders-for-collaborative","repo_url":"https://github.com/chenjiyan2001/Paddle-DeepRecommender","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"paddle","reach":null}],"tasks":[{"task_slug":"collaborative-filtering","task_name":"Collaborative Filtering"},{"task_slug":"recommendation-systems","task_name":"Recommendation Systems"}],"methods":[{"method_slug":"dropout","method_name":"Dropout"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1708.01715","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}