This repository contains Deep Learning based Articles , Papers and Repositories for Recommendation Systems.
- Relational Stacked Denoising Autoencoder for Tag Recommendation by Hao Wang, Xingjian Shi, and Dit-Yan Yeung. AAAI 2015
- Collaborative Deep Learning for Recommender Systems by Hao Wang, Naiyan Wang, and Dit-Yan Yeung. KDD 2015
Source: http://wanghao.in/CDL.htm, Code: https://github.com/js05212/CDL
- Collaborative Recurrent Autoencoder: Recommend while Learning to Fill in the Blanks by Hao Wang, Xingjian Shi, and Dit-Yan Yeung. NIPS 2016
- 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
- A Neural Autoregressive Approach to Collaborative Filtering by Yin Zheng et all.
- Collaborative Recurrent Neural Networks for Dynamic Recommender Systems by Young-Jun Ko. ACML 2016
- Hybrid Recommender System based on Autoencoders by Florian Strub . 2016
- Deep content-based music recommendation by Aaron van den Oord.
- DeepPlaylist: Using Recurrent Neural Networks to Predict Song Similarity by Anusha Balakrishnan.
- Hybrid music recommender using content-based and social information by Paulo Chiliguano .
- CONTENT-AWARE COLLABORATIVE MUSIC RECOMMENDATION USING PRE-TRAINED NEURAL NETWORKS.
- TransNets: Learning to Transform for Recommendation by Rose Catherine.
- Learning Distributed Representations from Reviews for Collaborative Filtering by Amjad Almahairi.
- Ask the GRU: Multi-task Learning for Deep Text Recommendations by T Bansal.
- A Multi-View Deep Learning Approach for Cross Domain User Modeling in Recommendation Systems by Ali Mamdouh Elkahky.
- Deep collaborative filtering via marginalized denoising auto-encoder by S Li.
- Joint deep modeling of users and items using reviews for recommendation by L Zheng.
- Hybrid Collaborative Filtering with Neural Networks by Strub Source: https://pdfs.semanticscholar.org/fcbd/179590c30127cafbd00fd7087b47818406bc.pdf
- Trust-aware Top-N Recommender Systems with Correlative Denoising Autoencoder by Y Pan.
- Neural Semantic Personalized Ranking for item cold-start recommendation by T Ebesu .
- Representation Learning of Users and Items for Review Rating Prediction Using Attention-based Convolutional Neural Network by S Seo.
- 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
- Deep Neural Networks for YouTube Recommendations by Paul Covington.
- Wide & Deep Learning for Recommender Systems by Heng-Tze Cheng.
- A Survey and Critique of Deep Learning on Recommender Systems by Lei Zheng.
- 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
- Meta-Prod2Vec - Product Embeddings Using Side-Information for Recommendation by Flavian Vasile.
- Representation Learning and Pairwise Ranking for Implicit and Explicit Feedback in Recommendation Systems by Mikhail Trofimov
- 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
- Collaborative Filtering with Recurrent Neural Networks by Robin Devooght
- Training Deep AutoEncoders for Collaborative Filtering by Oleksii Kuchaiev, Boris Ginsburg.
Source: https://arxiv.org/abs/1708.01715 , Code: https://github.com/NVIDIA/DeepRecommender
- 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
- 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
- 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
- Deep Session Interest Network for Click-Through Rate Prediction , Code : https://github.com/shenweichen/DeepCTR Source: https://arxiv.org/pdf/1905.06482v1.pdf
- AutoInt: Automatic Feature Interaction Learning via Self-Attentive Neural Networks, Code: https://github.com/shichence/AutoInt Source: https://arxiv.org/pdf/1810.11921v2.pdf
- 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
- Deep Learning Meets Recommendation Systems by Wann-Jiun.
- Machine Learning for Recommender systems Source: https://medium.com/recombee-blog/machine-learning-for-recommender-systems-part-1-algorithms-evaluation-and-cold-start-6f696683d0ed
- 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
- 2nd Workshop on Deep Learning for Recommender Systems , 27 August 2017. Como, Italy.
- THE AAAI-19 WORKSHOP ON RECOMMENDER SYSTEMS AND NATURAL LANGUAGE PROCESSING (RECNLP) Source: https://recnlp2019.github.io/
- The 4th Workshop on Health Recommender Systems co-located with ACM RecSys 2019 Source: https://healthrecsys.github.io/2019/
- Deep Learning for Recommender Systems by Balázs Hidasi. RecSys Summer School, 21-25 August, 2017, Bozen-Bolzano. Slides
- Deep Learning for Recommender Systems by Alexandros Karatzoglou and Balázs Hidasi. RecSys2017 Tutorial. Slides
- Introduction to recommender Systems by Miguel González-Fierro. Link
- Collaborative Filtering using a RBM by Big Data University. Link
- Building a Recommendation System in TensorFlow: Overview. Link
Spotlight: deep learning recommender systems in PyTorch that utilizes factorization model and sequence model in the back end
Amazon DSSTNE: deep learning library by amazon (specially for recommended systems i.e. sparse data)
Recoder: Large scale training of factorization models for Collaborative Filtering with PyTorch
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
- Practical Recommender Systems by Kim Falk (Manning Publications). Chapter 1 Source: https://www.manning.com/books/practical-recommender-systems
- Recommender Systems Handbook by Ricci, F. et al. Source: https://dl.acm.org/citation.cfm?id=1941884