{"id":15039011,"url":"https://github.com/robi56/deep-learning-for-recommendation-systems","last_synced_at":"2025-05-15T06:04:32.276Z","repository":{"id":41045442,"uuid":"90620225","full_name":"robi56/Deep-Learning-for-Recommendation-Systems","owner":"robi56","description":"This repository contains Deep Learning based articles , paper and repositories for Recommender Systems","archived":false,"fork":false,"pushed_at":"2020-02-27T05:52:08.000Z","size":31,"stargazers_count":2841,"open_issues_count":1,"forks_count":707,"subscribers_count":200,"default_branch":"master","last_synced_at":"2025-05-15T06:03:45.774Z","etag":null,"topics":["collaborative-filtering","deep-learning","hybrid-recommendation","machine-learning","music-recommendation","neural-network","python","recommender-system","tensorflow"],"latest_commit_sha":null,"homepage":"","language":null,"has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/robi56.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null}},"created_at":"2017-05-08T11:24:34.000Z","updated_at":"2025-05-11T17:47:05.000Z","dependencies_parsed_at":"2022-07-16T05:30:28.665Z","dependency_job_id":null,"html_url":"https://github.com/robi56/Deep-Learning-for-Recommendation-Systems","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/robi56%2FDeep-Learning-for-Recommendation-Systems","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/robi56%2FDeep-Learning-for-Recommendation-Systems/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/robi56%2FDeep-Learning-for-Recommendation-Systems/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/robi56%2FDeep-Learning-for-Recommendation-Systems/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/robi56","download_url":"https://codeload.github.com/robi56/Deep-Learning-for-Recommendation-Systems/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":254283339,"owners_count":22045140,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["collaborative-filtering","deep-learning","hybrid-recommendation","machine-learning","music-recommendation","neural-network","python","recommender-system","tensorflow"],"created_at":"2024-09-24T20:41:13.228Z","updated_at":"2025-05-15T06:04:32.258Z","avatar_url":"https://github.com/robi56.png","language":null,"funding_links":[],"categories":[],"sub_categories":[],"readme":"\n# Deep-Learning-for-Recommendation-Systems\nThis repository contains Deep Learning based Articles , Papers and Repositories for Recommendation Systems.\n## Papers\n\n1. Relational Stacked Denoising Autoencoder for Tag Recommendation by Hao Wang, Xingjian Shi, and Dit-Yan Yeung. AAAI 2015 \u003cbr\u003e\nSource:  http://wanghao.in/paper/AAAI15_RSDAE.pdf\n2. Collaborative Deep Learning for Recommender Systems by Hao Wang, Naiyan Wang, and Dit-Yan Yeung. KDD 2015 \u003cbr\u003e\nSource: http://wanghao.in/CDL.htm, Code: https://github.com/js05212/CDL\n3. Collaborative Recurrent Autoencoder: Recommend while Learning to Fill in the Blanks by Hao Wang, Xingjian Shi, and Dit-Yan Yeung. NIPS 2016 \u003cbr\u003e\nSource: https://papers.nips.cc/paper/6163-collaborative-recurrent-autoencoder-recommend-while-learning-to-fill-in-the-blanks\n4. Convolutional Matrix Factorization for Document Context-Aware Recommendation by Donghyun Kim, Chanyoung Park, Jinoh Oh, Seungyong Lee, Hwanjo Yu, RecSys 2016.\u003cbr\u003e\nSource: http://dm.postech.ac.kr/~cartopy/ConvMF/, Code: https://github.com/cartopy/ConvMF\n5. A Neural Autoregressive Approach to Collaborative Filtering by Yin Zheng et all.\u003cbr\u003e\nSource: http://proceedings.mlr.press/v48/zheng16.pdf\n6. Collaborative Recurrent Neural Networks for Dynamic Recommender Systems by Young-Jun Ko. ACML 2016 \u003cbr\u003e\nSource: http://proceedings.mlr.press/v63/ko101.pdf\n7. Hybrid Recommender System based on Autoencoders by Florian Strub . 2016 \u003cbr\u003e\nSource: https://arxiv.org/pdf/1606.07659.pdf\n8. Deep content-based music recommendation by Aaron van den Oord. \u003cbr\u003e\nSource: https://papers.nips.cc/paper/5004-deep-content-based-music-recommendation.pdf\n9. DeepPlaylist: Using Recurrent Neural Networks to Predict Song Similarity by Anusha Balakrishnan. \u003cbr\u003e\nSource: https://cs224d.stanford.edu/reports/BalakrishnanDixit.pdf\n10.  Hybrid music recommender using content-based and social information by  Paulo Chiliguano .\u003cbr\u003e\nSource: http://ieeexplore.ieee.org/document/7472151\n11. CONTENT-AWARE COLLABORATIVE MUSIC RECOMMENDATION USING PRE-TRAINED NEURAL NETWORKS. \u003cbr\u003e\nSource: http://ismir2015.uma.es/articles/290_Paper.pdf\n12.  TransNets: Learning to Transform for Recommendation  by Rose Catherine. \u003cbr\u003e\nSource: https://arxiv.org/abs/1704.02298 \n13. Learning Distributed Representations from Reviews for Collaborative Filtering by  \tAmjad Almahairi. \u003cbr\u003e \t\nSource: http://dl.acm.org/citation.cfm?id=2800192\n14. Ask the GRU: Multi-task Learning for Deep Text Recommendations by T Bansal. \u003cbr\u003e \nSource: https://arxiv.org/pdf/1609.02116.pdf\n15.   A Multi-View Deep Learning Approach for Cross Domain User Modeling in Recommendation Systems by Ali Mamdouh Elkahky.\u003cbr\u003e\nSource: http://sonyis.me/paperpdf/frp1159-songA-www-2015.pdf\n16. Deep collaborative filtering via marginalized denoising auto-encoder by S Li.\u003cbr\u003e\nSource: https://pdfs.semanticscholar.org/ff29/2f00055d8221c42d4831679db9d3872b6fbd.pdf\n17. Joint deep modeling of users and items using reviews for recommendation by L Zheng. \u003cbr\u003e\nSource: https://arxiv.org/pdf/1701.04783\n18. Hybrid Collaborative Filtering with Neural Networks by Strub \nSource: https://pdfs.semanticscholar.org/fcbd/179590c30127cafbd00fd7087b47818406bc.pdf\n19. Trust-aware Top-N Recommender Systems with Correlative Denoising Autoencoder by Y Pan. \u003cbr\u003e \nSource: https://arxiv.org/pdf/1703.01760\n20. Neural Semantic Personalized Ranking for item cold-start recommendation by T Ebesu . \u003cbr\u003e\nSource: http://www.cse.scu.edu/~yfang/NSPR.pdf\n21. Representation Learning of Users and Items for Review Rating Prediction Using Attention-based Convolutional Neural Network by S Seo. \u003cbr\u003e \nSource: http://mlrec.org/2017/papers/paper8.pdf\n22. Collaborative Denoising Auto-Encoders for Top-N Recommender Systems by Y Wu. \u003cbr\u003e\nSource: http://alicezheng.org/papers/wsdm16-cdae.pdf, Code: https://github.com/jasonyaw/CDAE\n23. Deep Neural Networks for YouTube Recommendations by Paul Covington. \u003cbr\u003e \nSource: https://static.googleusercontent.com/media/research.google.com/en//pubs/archive/45530.pdf\n24. Wide \u0026 Deep Learning for Recommender Systems by Heng-Tze Cheng.\u003cbr\u003e\nSource: https://arxiv.org/abs/1606.07792\n25. A Survey and Critique of Deep Learning on Recommender Systems by Lei Zheng.\u003cbr\u003e \nSource: http://bdsc.lab.uic.edu/docs/survey-critique-deep.pdf\n26. Restricted Boltzmann Machines for Collaborative Filtering by Ruslan Salakhutdinov. \u003cbr\u003e\nSource: http://www.machinelearning.org/proceedings/icml2007/papers/407.pdf , Code: https://github.com/felipecruz/CFRBM\n27. Meta-Prod2Vec - Product Embeddings Using Side-Information for Recommendation by Flavian Vasile. \u003cbr\u003e\nSource: https://arxiv.org/pdf/1607.07326.pdf\n28.  Representation Learning and Pairwise Ranking for Implicit and Explicit Feedback in Recommendation Systems by Mikhail Trofimov \u003cbr\u003e\nSource: https://arxiv.org/abs/1705.00105\n29. DeepFM: A Factorization-Machine based Neural Network for CTR Prediction. IJCAI2017 \u003cbr\u003e Source:  https://arxiv.org/abs/1703.04247 , Code (provided by readers): https://github.com/Leavingseason/OpenLearning4DeepRecsys\n30. Collaborative Filtering with Recurrent Neural Networks by Robin Devooght \u003cbr\u003e Source:  https://arxiv.org/pdf/1608.07400.pdf\n31. Training Deep AutoEncoders for Collaborative Filtering by Oleksii Kuchaiev, Boris Ginsburg. \u003cbr\u003e Source: https://arxiv.org/abs/1708.01715 , Code: https://github.com/NVIDIA/DeepRecommender\n32. Collaborative Variational Autoencoder for Recommender\nSystems by Xiaopeng Li and James She \u003cbr\u003e Source: http://eelxpeng.github.io/assets/paper/Collaborative_Variational_Autoencoder.pdf, Code: https://github.com/eelxpeng/CollaborativeVAE\n33. Variational Autoencoders for Collaborative Filtering by Dawen Liang, Rahul G. Krishnan, Matthew D. Hoffman and Tony Jebara \u003cbr\u003e Source: https://arxiv.org/pdf/1802.05814.pdf, Code: https://github.com/dawenl/vae_cf\n34. Neural Collaborative Filtering by Xiangnan He, Lizi Liao, Hanwang Zhang, Liqiang Nie, Xia Hu and Tat-Seng Chua \u003cbr\u003e Source: https://www.comp.nus.edu.sg/~xiangnan/papers/ncf.pdf , Code : https://github.com/hexiangnan/neural_collaborative_filtering\n Source: https://arxiv.org/abs/1708.05031\n35. Deep Session Interest Network for Click-Through Rate Prediction , Code : https://github.com/shenweichen/DeepCTR \nSource: https://arxiv.org/pdf/1905.06482v1.pdf\n36. AutoInt: Automatic Feature Interaction Learning via Self-Attentive Neural Networks, Code: https://github.com/shichence/AutoInt \nSource: https://arxiv.org/pdf/1810.11921v2.pdf\n37. Product-based Neural Networks for User Response Prediction over Multi-field Categorical Data, Code: https://github.com/Atomu2014/product-nets-distributed\nSource: https://arxiv.org/abs/1807.00311\n\n\n## Blogs\n1. Deep Learning Meets Recommendation Systems by Wann-Jiun. \u003cbr\u003e\nSource: https://blog.nycdatascience.com/student-works/deep-learning-meets-recommendation-systems/\n2. Machine Learning for Recommender systems\nSource: https://medium.com/recombee-blog/machine-learning-for-recommender-systems-part-1-algorithms-evaluation-and-cold-start-6f696683d0ed\n3. Check out our new client-side integration support and deploy personalized recommendations faster\nSource: https://medium.com/recombee-blog/check-out-our-new-client-side-integration-support-and-deploy-personalized-recommendations-faster-7dd7bf5b6241\n\n\n## Workshops \n1. 2nd Workshop on Deep Learning for Recommender Systems , 27 August 2017. Como, Italy.\u003cbr\u003e \nSource: http://dlrs-workshop.org\n2. THE AAAI-19 WORKSHOP ON RECOMMENDER SYSTEMS AND NATURAL LANGUAGE PROCESSING (RECNLP)\nSource: https://recnlp2019.github.io/\n3. The 4th Workshop on Health Recommender Systems co-located with ACM RecSys 2019\nSource: https://healthrecsys.github.io/2019/\n\n## Tutorials\n1. Deep Learning for Recommender Systems by Balázs Hidasi. [RecSys Summer School](http://pro.unibz.it/projects/schoolrecsys17/program.html), 21-25 August, 2017, Bozen-Bolzano. [Slides](https://www.slideshare.net/balazshidasi/deep-learning-in-recommender-systems-recsys-summer-school-2017)\n2. Deep Learning for Recommender Systems by Alexandros\tKaratzoglou and Balázs\tHidasi. RecSys2017 Tutorial. [Slides](https://www.slideshare.net/kerveros99/deep-learning-for-recommender-systems-recsys2017-tutorial)\n3. Introduction to recommender Systems by Miguel González-Fierro. [Link](https://github.com/miguelgfierro/sciblog_support/blob/master/Intro_to_Recommendation_Systems/Intro_Recommender.ipynb)\n4. Collaborative Filtering using a RBM by Big Data University. [Link](https://github.com/santipuch590/deeplearning-tf/blob/master/dl_tf_BDU/4.RBM/ML0120EN-4.2-Review-CollaborativeFilteringwithRBM.ipynb)\n5. Building a Recommendation System in TensorFlow: Overview. [Link](https://cloud.google.com/solutions/machine-learning/recommendation-system-tensorflow-overview)\n\n## Software\n1. Spotlight: deep learning recommender systems in PyTorch that utilizes factorization model and sequence model in the back end \u003cbr\u003e\nSource: https://github.com/maciejkula/spotlight\n\n2. Amazon DSSTNE: deep learning library by amazon (specially for recommended systems i.e. sparse data) \u003cbr\u003e\nSource: https://github.com/amzn/amazon-dsstne\n\n3. Recoder: Large scale training of factorization models for Collaborative Filtering with PyTorch \u003cbr\u003e\nSource: https://github.com/amoussawi/recoder\n\n4. PredictionIO is built on technologies Apache Spark, Apache HBase and Spray. It is a machine learning server that can be used to create a recommender system. The source can be located on github and it looks very active. \nSource: https://github.com/apache/predictionio\n\n## Books\n1. Practical Recommender Systems by Kim Falk (Manning Publications). Chapter 1 \nSource: https://www.manning.com/books/practical-recommender-systems\n2. Recommender Systems Handbook by Ricci, F. et al.\nSource: https://dl.acm.org/citation.cfm?id=1941884\n\n \n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Frobi56%2Fdeep-learning-for-recommendation-systems","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Frobi56%2Fdeep-learning-for-recommendation-systems","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Frobi56%2Fdeep-learning-for-recommendation-systems/lists"}