└── README.md


/README.md:
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  1 | 
  2 | # Deep-Learning-for-Recommendation-Systems
  3 | This repository contains Deep Learning based Articles , Papers and Repositories for Recommendation Systems.
  4 | ## Papers
  5 | 
  6 | 1. Relational Stacked Denoising Autoencoder for Tag Recommendation by Hao Wang, Xingjian Shi, and Dit-Yan Yeung. AAAI 2015 <br>
  7 | Source:  http://wanghao.in/paper/AAAI15_RSDAE.pdf
  8 | 2. Collaborative Deep Learning for Recommender Systems by Hao Wang, Naiyan Wang, and Dit-Yan Yeung. KDD 2015 <br>
  9 | Source: http://wanghao.in/CDL.htm, Code: https://github.com/js05212/CDL
 10 | 3. Collaborative Recurrent Autoencoder: Recommend while Learning to Fill in the Blanks by Hao Wang, Xingjian Shi, and Dit-Yan Yeung. NIPS 2016 <br>
 11 | Source: https://papers.nips.cc/paper/6163-collaborative-recurrent-autoencoder-recommend-while-learning-to-fill-in-the-blanks
 12 | 4. Convolutional Matrix Factorization for Document Context-Aware Recommendation by Donghyun Kim, Chanyoung Park, Jinoh Oh, Seungyong Lee, Hwanjo Yu, RecSys 2016.<br>
 13 | Source: http://dm.postech.ac.kr/~cartopy/ConvMF/, Code: https://github.com/cartopy/ConvMF
 14 | 5. A Neural Autoregressive Approach to Collaborative Filtering by Yin Zheng et all.<br>
 15 | Source: http://proceedings.mlr.press/v48/zheng16.pdf
 16 | 6. Collaborative Recurrent Neural Networks for Dynamic Recommender Systems by Young-Jun Ko. ACML 2016 <br>
 17 | Source: http://proceedings.mlr.press/v63/ko101.pdf
 18 | 7. Hybrid Recommender System based on Autoencoders by Florian Strub . 2016 <br>
 19 | Source: https://arxiv.org/pdf/1606.07659.pdf
 20 | 8. Deep content-based music recommendation by Aaron van den Oord. <br>
 21 | Source: https://papers.nips.cc/paper/5004-deep-content-based-music-recommendation.pdf
 22 | 9. DeepPlaylist: Using Recurrent Neural Networks to Predict Song Similarity by Anusha Balakrishnan. <br>
 23 | Source: https://cs224d.stanford.edu/reports/BalakrishnanDixit.pdf
 24 | 10.  Hybrid music recommender using content-based and social information by  Paulo Chiliguano .<br>
 25 | Source: http://ieeexplore.ieee.org/document/7472151
 26 | 11. CONTENT-AWARE COLLABORATIVE MUSIC RECOMMENDATION USING PRE-TRAINED NEURAL NETWORKS. <br>
 27 | Source: http://ismir2015.uma.es/articles/290_Paper.pdf
 28 | 12.  TransNets: Learning to Transform for Recommendation  by Rose Catherine. <br>
 29 | Source: https://arxiv.org/abs/1704.02298 
 30 | 13. Learning Distributed Representations from Reviews for Collaborative Filtering by  	Amjad Almahairi. <br> 	
 31 | Source: http://dl.acm.org/citation.cfm?id=2800192
 32 | 14. Ask the GRU: Multi-task Learning for Deep Text Recommendations by T Bansal. <br> 
 33 | Source: https://arxiv.org/pdf/1609.02116.pdf
 34 | 15.   A Multi-View Deep Learning Approach for Cross Domain User Modeling in Recommendation Systems by Ali Mamdouh Elkahky.<br>
 35 | Source: http://sonyis.me/paperpdf/frp1159-songA-www-2015.pdf
 36 | 16. Deep collaborative filtering via marginalized denoising auto-encoder by S Li.<br>
 37 | Source: https://pdfs.semanticscholar.org/ff29/2f00055d8221c42d4831679db9d3872b6fbd.pdf
 38 | 17. Joint deep modeling of users and items using reviews for recommendation by L Zheng. <br>
 39 | Source: https://arxiv.org/pdf/1701.04783
 40 | 18. Hybrid Collaborative Filtering with Neural Networks by Strub 
 41 | Source: https://pdfs.semanticscholar.org/fcbd/179590c30127cafbd00fd7087b47818406bc.pdf
 42 | 19. Trust-aware Top-N Recommender Systems with Correlative Denoising Autoencoder by Y Pan. <br> 
 43 | Source: https://arxiv.org/pdf/1703.01760
 44 | 20. Neural Semantic Personalized Ranking for item cold-start recommendation by T Ebesu . <br>
 45 | Source: http://www.cse.scu.edu/~yfang/NSPR.pdf
 46 | 21. Representation Learning of Users and Items for Review Rating Prediction Using Attention-based Convolutional Neural Network by S Seo. <br> 
 47 | Source: http://mlrec.org/2017/papers/paper8.pdf
 48 | 22. Collaborative Denoising Auto-Encoders for Top-N Recommender Systems by Y Wu. <br>
 49 | Source: http://alicezheng.org/papers/wsdm16-cdae.pdf, Code: https://github.com/jasonyaw/CDAE
 50 | 23. Deep Neural Networks for YouTube Recommendations by Paul Covington. <br> 
 51 | Source: https://static.googleusercontent.com/media/research.google.com/en//pubs/archive/45530.pdf
 52 | 24. Wide & Deep Learning for Recommender Systems by Heng-Tze Cheng.<br>
 53 | Source: https://arxiv.org/abs/1606.07792
 54 | 25. A Survey and Critique of Deep Learning on Recommender Systems by Lei Zheng.<br> 
 55 | Source: http://bdsc.lab.uic.edu/docs/survey-critique-deep.pdf
 56 | 26. Restricted Boltzmann Machines for Collaborative Filtering by Ruslan Salakhutdinov. <br>
 57 | Source: http://www.machinelearning.org/proceedings/icml2007/papers/407.pdf , Code: https://github.com/felipecruz/CFRBM
 58 | 27. Meta-Prod2Vec - Product Embeddings Using Side-Information for Recommendation by Flavian Vasile. <br>
 59 | Source: https://arxiv.org/pdf/1607.07326.pdf
 60 | 28.  Representation Learning and Pairwise Ranking for Implicit and Explicit Feedback in Recommendation Systems by Mikhail Trofimov <br>
 61 | Source: https://arxiv.org/abs/1705.00105
 62 | 29. DeepFM: A Factorization-Machine based Neural Network for CTR Prediction. IJCAI2017 <br> Source:  https://arxiv.org/abs/1703.04247 , Code (provided by readers): https://github.com/Leavingseason/OpenLearning4DeepRecsys
 63 | 30. Collaborative Filtering with Recurrent Neural Networks by Robin Devooght <br> Source:  https://arxiv.org/pdf/1608.07400.pdf
 64 | 31. Training Deep AutoEncoders for Collaborative Filtering by Oleksii Kuchaiev, Boris Ginsburg. <br> Source: https://arxiv.org/abs/1708.01715 , Code: https://github.com/NVIDIA/DeepRecommender
 65 | 32. Collaborative Variational Autoencoder for Recommender
 66 | Systems by Xiaopeng Li and James She <br> Source: http://eelxpeng.github.io/assets/paper/Collaborative_Variational_Autoencoder.pdf, Code: https://github.com/eelxpeng/CollaborativeVAE
 67 | 33. Variational Autoencoders for Collaborative Filtering by Dawen Liang, Rahul G. Krishnan, Matthew D. Hoffman and Tony Jebara <br> Source: https://arxiv.org/pdf/1802.05814.pdf, Code: https://github.com/dawenl/vae_cf
 68 | 34. Neural Collaborative Filtering by Xiangnan He, Lizi Liao, Hanwang Zhang, Liqiang Nie, Xia Hu and Tat-Seng Chua <br> Source: https://www.comp.nus.edu.sg/~xiangnan/papers/ncf.pdf , Code : https://github.com/hexiangnan/neural_collaborative_filtering
 69 |  Source: https://arxiv.org/abs/1708.05031
 70 | 35. Deep Session Interest Network for Click-Through Rate Prediction , Code : https://github.com/shenweichen/DeepCTR 
 71 | Source: https://arxiv.org/pdf/1905.06482v1.pdf
 72 | 36. AutoInt: Automatic Feature Interaction Learning via Self-Attentive Neural Networks, Code: https://github.com/shichence/AutoInt 
 73 | Source: https://arxiv.org/pdf/1810.11921v2.pdf
 74 | 37. Product-based Neural Networks for User Response Prediction over Multi-field Categorical Data, Code: https://github.com/Atomu2014/product-nets-distributed
 75 | Source: https://arxiv.org/abs/1807.00311
 76 | 
 77 | 
 78 | ## Blogs
 79 | 1. Deep Learning Meets Recommendation Systems by Wann-Jiun. <br>
 80 | Source: https://blog.nycdatascience.com/student-works/deep-learning-meets-recommendation-systems/
 81 | 2. Machine Learning for Recommender systems
 82 | Source: https://medium.com/recombee-blog/machine-learning-for-recommender-systems-part-1-algorithms-evaluation-and-cold-start-6f696683d0ed
 83 | 3. Check out our new client-side integration support and deploy personalized recommendations faster
 84 | Source: https://medium.com/recombee-blog/check-out-our-new-client-side-integration-support-and-deploy-personalized-recommendations-faster-7dd7bf5b6241
 85 | 
 86 | 
 87 | ## Workshops 
 88 | 1. 2nd Workshop on Deep Learning for Recommender Systems , 27 August 2017. Como, Italy.<br> 
 89 | Source: http://dlrs-workshop.org
 90 | 2. THE AAAI-19 WORKSHOP ON RECOMMENDER SYSTEMS AND NATURAL LANGUAGE PROCESSING (RECNLP)
 91 | Source: https://recnlp2019.github.io/
 92 | 3. The 4th Workshop on Health Recommender Systems co-located with ACM RecSys 2019
 93 | Source: https://healthrecsys.github.io/2019/
 94 | 
 95 | ## Tutorials
 96 | 1. 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)
 97 | 2. Deep Learning for Recommender Systems by Alexandros	Karatzoglou and Balázs	Hidasi. RecSys2017 Tutorial. [Slides](https://www.slideshare.net/kerveros99/deep-learning-for-recommender-systems-recsys2017-tutorial)
 98 | 3. Introduction to recommender Systems by Miguel González-Fierro. [Link](https://github.com/miguelgfierro/sciblog_support/blob/master/Intro_to_Recommendation_Systems/Intro_Recommender.ipynb)
 99 | 4. 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)
100 | 5. Building a Recommendation System in TensorFlow: Overview. [Link](https://cloud.google.com/solutions/machine-learning/recommendation-system-tensorflow-overview)
101 | 
102 | ## Software
103 | 1. Spotlight: deep learning recommender systems in PyTorch that utilizes factorization model and sequence model in the back end <br>
104 | Source: https://github.com/maciejkula/spotlight
105 | 
106 | 2. Amazon DSSTNE: deep learning library by amazon (specially for recommended systems i.e. sparse data) <br>
107 | Source: https://github.com/amzn/amazon-dsstne
108 | 
109 | 3. Recoder: Large scale training of factorization models for Collaborative Filtering with PyTorch <br>
110 | Source: https://github.com/amoussawi/recoder
111 | 
112 | 4. 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. 
113 | Source: https://github.com/apache/predictionio
114 | 
115 | ## Books
116 | 1. Practical Recommender Systems by Kim Falk (Manning Publications). Chapter 1 
117 | Source: https://www.manning.com/books/practical-recommender-systems
118 | 2. Recommender Systems Handbook by Ricci, F. et al.
119 | Source: https://dl.acm.org/citation.cfm?id=1941884
120 | 
121 |  
122 | 


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