This repository contains Deep Learning based Articles , Papers and Repositories for Recommendation Systems.
- 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.
Source: http://proceedings.mlr.press/v48/zheng16.pdf - Collaborative Recurrent Neural Networks for Dynamic Recommender Systems by Young-Jun Ko. ACML 2016
Source: http://proceedings.mlr.press/v63/ko101.pdf - Hybrid Recommender System based on Autoencoders by Florian Strub . 2016
Source: https://arxiv.org/pdf/1606.07659.pdf - Deep content-based music recommendation by Aaron van den Oord.
Source: https://papers.nips.cc/paper/5004-deep-content-based-music-recommendation.pdf - DeepPlaylist: Using Recurrent Neural Networks to Predict Song Similarity by Anusha Balakrishnan.
Source: https://cs224d.stanford.edu/reports/BalakrishnanDixit.pdf - Hybrid music recommender using content-based and social information by Paulo Chiliguano .
Source: http://ieeexplore.ieee.org/document/7472151 - CONTENT-AWARE COLLABORATIVE MUSIC RECOMMENDATION USING PRE-TRAINED NEURAL NETWORKS.
Source: http://ismir2015.uma.es/articles/290_Paper.pdf - TransNets: Learning to Transform for Recommendation by Rose Catherine.
Source: https://arxiv.org/abs/1704.02298 - Learning Distributed Representations from Reviews for Collaborative Filtering by Amjad Almahairi.
Source: http://dl.acm.org/citation.cfm?id=2800192 - Ask the GRU: Multi-task Learning for Deep Text Recommendations by T Bansal.
Source: https://arxiv.org/pdf/1609.02116.pdf - 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 - Deep collaborative filtering via marginalized denoising auto-encoder by S Li.
Source: https://pdfs.semanticscholar.org/ff29/2f00055d8221c42d4831679db9d3872b6fbd.pdf - Joint deep modeling of users and items using reviews for recommendation by L Zheng.
Source: https://arxiv.org/pdf/1701.04783 - 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.
Source: https://arxiv.org/pdf/1703.01760 - Neural Semantic Personalized Ranking for item cold-start recommendation by T Ebesu .
Source: http://www.cse.scu.edu/~yfang/NSPR.pdf - 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 - 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.
Source: https://static.googleusercontent.com/media/research.google.com/en//pubs/archive/45530.pdf - Wide & Deep Learning for Recommender Systems by Heng-Tze Cheng.
Source: https://arxiv.org/abs/1606.07792 - A Survey and Critique of Deep Learning on Recommender Systems by Lei Zheng.
Source: http://bdsc.lab.uic.edu/docs/survey-critique-deep.pdf - 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.
Source: https://arxiv.org/pdf/1607.07326.pdf - Representation Learning and Pairwise Ranking for Implicit and Explicit Feedback in Recommendation Systems by Mikhail Trofimov
Source: https://arxiv.org/abs/1705.00105 - 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
Source: https://arxiv.org/pdf/1608.07400.pdf - 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
- Deep Learning Meets Recommendation Systems by Wann-Jiun.
Source: https://blog.nycdatascience.com/student-works/deep-learning-meets-recommendation-systems/
- 2nd Workshop on Deep Learning for Recommender Systems , 27 August 2017. Como, Italy.
Source: http://dlrs-workshop.org
- 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
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Spotlight: deep learning recommender systems in PyTorch
Source: https://github.com/maciejkula/spotlight -
Amazon DSSTNE: deep learning library by amazon (specially for recommended systems i.e. sparse data)
Source: https://github.com/amzn/amazon-dsstne