Deep-Learning-for-Recommendation-Systems

This repository contains Deep Learning based Articles , Papers and Repositories for Recommendation Systems.

Papers

1. Convolutional Matrix Factorization for Document Context-Aware Recommendation by Donghyun Kim, Chanyoung Park, Jinoh Oh, Seungyong Lee, Hwanjo Yu, RecSys 2016.
   Source: http://dm.postech.ac.kr/~cartopy/ConvMF/, Code: https://github.com/cartopy/ConvMF
2. A Neural Autoregressive Approach to Collaborative Filtering by Yin Zheng et all.
   Source: http://proceedings.mlr.press/v48/zheng16.pdf
3. Collaborative Recurrent Neural Networks for Dynamic Recommender Systems by Young-Jun Ko. ACML 2016
   Source: http://proceedings.mlr.press/v63/ko101.pdf
4. Hybrid Recommender System based on Autoencoders by Florian Strub . 2016
   Source: https://arxiv.org/pdf/1606.07659.pdf
5. Deep content-based music recommendation by Aaron van den Oord.
   Source: https://papers.nips.cc/paper/5004-deep-content-based-music-recommendation.pdf
6. DeepPlaylist: Using Recurrent Neural Networks to Predict Song Similarity by Anusha Balakrishnan.
   Source: https://cs224d.stanford.edu/reports/BalakrishnanDixit.pdf
7. Hybrid music recommender using content-based and social information by Paulo Chiliguano .
   Source: http://ieeexplore.ieee.org/document/7472151
8. CONTENT-AWARE COLLABORATIVE MUSIC RECOMMENDATION USING PRE-TRAINED NEURAL NETWORKS.
   Source: http://ismir2015.uma.es/articles/290_Paper.pdf
9. TransNets: Learning to Transform for Recommendation by Rose Catherine.
   Source: https://arxiv.org/abs/1704.02298
10. Learning Distributed Representations from Reviews for Collaborative Filtering by Amjad Almahairi.
    Source: http://dl.acm.org/citation.cfm?id=2800192
11. Ask the GRU: Multi-task Learning for Deep Text Recommendations by T Bansal.
    Source: https://arxiv.org/pdf/1609.02116.pdf
12. A Multi-View Deep Learning Approach for Cross Domain User Modeling in Recommendation Systems by Ali Mamdouh Elkahky.
    Source: http://sonyis.me/paperpdf/frp1159-songA-www-2015.pdf
13. Deep collaborative filtering via marginalized denoising auto-encoder by S Li.
    Source: https://pdfs.semanticscholar.org/ff29/2f00055d8221c42d4831679db9d3872b6fbd.pdf
14. Joint deep modeling of users and items using reviews for recommendation by L Zheng.
    Source: https://arxiv.org/pdf/1701.04783
15. Hybrid Collaborative Filtering with Neural Networks by Strub
    Source: https://pdfs.semanticscholar.org/fcbd/179590c30127cafbd00fd7087b47818406bc.pdf
16. Trust-aware Top-N Recommender Systems with Correlative Denoising Autoencoder by Y Pan.
    Source: https://arxiv.org/pdf/1703.01760
17. Neural Semantic Personalized Ranking for item cold-start recommendation by T Ebesu .
    Source: http://www.cse.scu.edu/~yfang/NSPR.pdf
18. Representation Learning of Users and Items for Review Rating Prediction Using Attention-based Convolutional Neural Network by S Seo.
    Source: http://mlrec.org/2017/papers/paper8.pdf
19. Collaborative Denoising Auto-Encoders for Top-N Recommender Systems by Y Wu.
    Source: http://alicezheng.org/papers/wsdm16-cdae.pdf, Code: https://github.com/jasonyaw/CDAE
20. Deep Neural Networks for YouTube Recommendations by Paul Covington.
    Source: https://static.googleusercontent.com/media/research.google.com/en//pubs/archive/45530.pdf
21. Wide & Deep Learning for Recommender Systems by Heng-Tze Cheng.
    Source: https://arxiv.org/abs/1606.07792
22. A Survey and Critique of Deep Learning on Recommender Systems by Lei Zheng.
    Source: http://bdsc.lab.uic.edu/docs/survey-critique-deep.pdf
23. Restricted Boltzmann Machines for Collaborative Filtering by Ruslan Salakhutdinov.
    Source: http://www.machinelearning.org/proceedings/icml2007/papers/407.pdf , Code: https://github.com/felipecruz/CFRBM
24. Meta-Prod2Vec - Product Embeddings Using Side-Information for Recommendation by Flavian Vasile.
    Source: https://arxiv.org/pdf/1607.07326.pdf
25. Representation Learning and Pairwise Ranking for Implicit and Explicit Feedback in Recommendation Systems by Mikhail Trofimov
    Source: https://arxiv.org/abs/1705.00105
26. DeepFM: A Factorization-Machine based Neural Network for CTR Prediction. IJCAI2017 Source:  https://arxiv.org/abs/1703.04247 , Code (provided by readers): https://github.com/Leavingseason/OpenLearning4DeepRecsys
27. Collaborative Filtering with Recurrent Neural Networks by Robin Devooght Source:  https://arxiv.org/pdf/1608.07400.pdf
28. Training Deep AutoEncoders for Collaborative Filtering by Oleksii Kuchaiev, Boris Ginsburg. Source: https://arxiv.org/abs/1708.01715 , Code: https://github.com/NVIDIA/DeepRecommender
29. Collaborative Variational Autoencoder for Recommender
    Systems by Xiaopeng Li and James She Source: http://eelxpeng.github.io/assets/paper/Collaborative_Variational_Autoencoder.pdf, Code: https://github.com/eelxpeng/CollaborativeVAE
30. Variational Autoencoders for Collaborative Filtering by Dawen Liang, Rahul G. Krishnan, Matthew D. Hoffman and Tony Jebara Source: https://arxiv.org/pdf/1802.05814.pdf, Code: https://github.com/dawenl/vae_cf
31. Neural Collaborative Filtering by Xiangnan He, Lizi Liao, Hanwang Zhang, Liqiang Nie, Xia Hu and Tat-Seng Chua Source: https://www.comp.nus.edu.sg/~xiangnan/papers/ncf.pdf , Code : https://github.com/hexiangnan/neural_collaborative_filtering
    Source: https://arxiv.org/abs/1708.05031
32. Deep Session Interest Network for Click-Through Rate Prediction , Code : https://github.com/shenweichen/DeepCTR
    Source: https://arxiv.org/pdf/1905.06482v1.pdf
33. AutoInt: Automatic Feature Interaction Learning via Self-Attentive Neural Networks, Code: https://github.com/shichence/AutoInt
    Source: https://arxiv.org/pdf/1810.11921v2.pdf
34. Product-based Neural Networks for User Response Prediction over Multi-field Categorical Data, Code: https://github.com/Atomu2014/product-nets-distributed
    Source: https://arxiv.org/abs/1807.00311

Blogs

1. Deep Learning Meets Recommendation Systems by Wann-Jiun.
   Source: https://blog.nycdatascience.com/student-works/deep-learning-meets-recommendation-systems/
2. Machine Learning for Recommender systems
   Source: https://medium.com/recombee-blog/machine-learning-for-recommender-systems-part-1-algorithms-evaluation-and-cold-start-6f696683d0ed
3. Check out our new client-side integration support and deploy personalized recommendations faster
   Source: https://medium.com/recombee-blog/check-out-our-new-client-side-integration-support-and-deploy-personalized-recommendations-faster-7dd7bf5b6241

Workshops

1. 2nd Workshop on Deep Learning for Recommender Systems , 27 August 2017. Como, Italy.
   Source: http://dlrs-workshop.org
2. THE AAAI-19 WORKSHOP ON RECOMMENDER SYSTEMS AND NATURAL LANGUAGE PROCESSING (RECNLP)
   Source: https://recnlp2019.github.io/
3. The 4th Workshop on Health Recommender Systems co-located with ACM RecSys 2019
   Source: https://healthrecsys.github.io/2019/

Tutorials

1. Deep Learning for Recommender Systems by Balázs Hidasi. RecSys Summer School, 21-25 August, 2017, Bozen-Bolzano. Slides
2. Deep Learning for Recommender Systems by Alexandros Karatzoglou and Balázs Hidasi. RecSys2017 Tutorial. Slides
3. Introduction to recommender Systems by Miguel González-Fierro. Link
4. Collaborative Filtering using a RBM by Big Data University. Link
5. Building a Recommendation System in TensorFlow: Overview. Link

Software

1. Spotlight: deep learning recommender systems in PyTorch that utilizes factorization model and sequence model in the back end
   Source: https://github.com/maciejkula/spotlight

2. Amazon DSSTNE: deep learning library by amazon (specially for recommended systems i.e. sparse data)
   Source: https://github.com/amzn/amazon-dsstne

3. Recoder: Large scale training of factorization models for Collaborative Filtering with PyTorch
   Source: https://github.com/amoussawi/recoder

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.
   Source: https://github.com/apache/predictionio

Books

1. Practical Recommender Systems by Kim Falk (Manning Publications). Chapter 1
   Source: https://www.manning.com/books/practical-recommender-systems
2. Recommender Systems Handbook by Ricci, F. et al.
   Source: https://dl.acm.org/citation.cfm?id=1941884