/Deep-Learning-for-Recommendation-Systems

This repository contains Deep Learning based articles , paper and repositories for Recommender Systems

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
  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
  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

Blogs

  1. Deep Learning Meets Recommendation Systems by Wann-Jiun.
    Source: https://blog.nycdatascience.com/student-works/deep-learning-meets-recommendation-systems/

Workshops

  1. 2nd Workshop on Deep Learning for Recommender Systems , 27 August 2017. Como, Italy.
    Source: http://dlrs-workshop.org