Recommendation System

Tutorials

Making a Contextual Recommendation Engine

• intro: by Muktabh Mayank
• youtube: https://www.youtube.com/watch?v=ToTyNF9kXkk&hd=1http://weibo.com/1402400261/profile?topnav=1&wvr=6
• video: http://pan.baidu.com/s/1eQFFVns

Papers

Collaborative Deep Learning for Recommender Systems

• arxiv: https://arxiv.org/abs/1409.2944
• paper: http://www.wanghao.in/paper/KDD15_CDL.pdf

Image-based recommendations on styles and substitutes

• paper: http://cseweb.ucsd.edu/~jmcauley/pdfs/sigir15.pdf
• code: http://cseweb.ucsd.edu/~jmcauley/code/imageGraph.tar.gz
• data: http://jmcauley.ucsd.edu/data/amazon/

A Complex Network Approach for Collaborative Recommendation

• arxiv: http://arxiv.org/abs/1510.00585v1

Session-based Recommendations with Recurrent Neural Networks

• intro: ICLR 2016
• arxiv: http://arxiv.org/abs/1511.06939
• github: https://github.com/hidasib/GRU4Rec

Item2Vec: Neural Item Embedding for Collaborative Filtering

• arxiv: https://arxiv.org/abs/1603.04259

Wide & Deep Learning for Recommender Systems

• intro: Google Research
• arxiv: http://arxiv.org/abs/1606.07792
• blog: https://research.googleblog.com/2016/06/wide-deep-learning-better-together-with.html

Hybrid Recommender System based on Autoencoders

• arxiv: https://arxiv.org/abs/1606.07659
• github: https://github.com/fstrub95/Autoencoders_cf
• notes: https://github.com/jxieeducation/DIY-Data-Science/blob/master/papernotes/2016/06/hybrid-recommender-system-based-on-autoencoders.md

Parallel Recurrent Neural Network Architectures for Feature-rich Session-based Recommendations

• paper: https://alexiskz.files.wordpress.com/2016/06/feature-rnn-paper1.pdf

Collaborative Filtering with Recurrent Neural Networks

• keywords: LSTM, movie recommendation
• arixv: http://arxiv.org/abs/1608.07400

Deep Neural Networks for YouTube Recommendations

• intro: RECSYS 2016. Google
• paper: http://static.googleusercontent.com/media/research.google.com/en//pubs/archive/45530.pdf
• summary: https://blog.acolyer.org/2016/09/19/deep-neural-networks-for-youtube-recommendations/

Photo Filter Recommendation by Category-Aware Aesthetic Learning

• intro: Filter Aesthetic Comparison Dataset (FACD): 28,000 filtered images and 42,240 reliable image pairs with aesthetic comparison annotations
• arxiv: http://arxiv.org/abs/1608.05339

Convolutional Matrix Factorization for Document Context-Aware Recommendation

• project page: http://dm.postech.ac.kr/~cartopy/ConvMF/
• paper: http://dl.acm.org/citation.cfm?id=2959165

Deep learning for audio-based music recommendation

• slides: https://docs.google.com/presentation/d/1CRSAs2WOKo5mFhh5Iu-xkDfyJsg_NDL1r5dRtj6_aHo/edit#slide=id.p
• mirror: https://pan.baidu.com/s/1o8NaMPs

Ask the GRU: Multi-Task Learning for Deep Text Recommendations

• arxiv: https://arxiv.org/abs/1609.02116

Collaborative Recurrent Autoencoder: Recommend while Learning to Fill in the Blanks

• intro: NIPS 2016
• arxiv: https://arxiv.org/abs/1611.00454

Recurrent Recommender Networks

• intro: University of Texas at Austin & Google Research & CMU & LinkedIn
• paper: http://alexbeutel.com/papers/rrn_wsdm2017.pdf

Deep Learning based Large Scale Visual Recommendation and Search for E-Commerce

• intro: Visnet. Flipkart’s visual search and recommendation system
• arxiv: https://arxiv.org/abs/1703.02344
• github: https://github.com/flipkart-incubator/fk-visual-search

What Your Image Reveals: Exploiting Visual Contents for Point-of-Interest Recommendation

• intro: Arizona State University & Michigan State University
• intro: Point-of-Interest (POI)
• paper: http://www.public.asu.edu/~swang187/publications/VPOI.pdf

Recurrent Neural Networks with Top-k Gains for Session-based Recommendations

• intro: Gravity R&D & Telefonica Research
• arxiv: https://arxiv.org/abs/1706.03847
• github: https://github.com/hidasib/GRU4Rec

On Sampling Strategies for Neural Network-based Collaborative Filtering

• intro: KDD 2017. University of California, Los Angeles & Yahoo! Research & Etsy Inc
• arxiv: https://arxiv.org/abs/1706.07881

Deep Learning based Recommender System: A Survey and New Perspectives

• intro: University of New South Wales & Nanyang Technological University
• arxiv: https://arxiv.org/abs/1707.07435

Training Deep AutoEncoders for Collaborative Filtering

• arxiv: https://arxiv.org/abs/1708.01715
• github: https://github.com/NVIDIA/DeepRecommender

Deep Collaborative Autoencoder for Recommender Systems: A Unified Framework for Explicit and Implicit Feedback

• intro: Zhejiang University
• arxiv: https://arxiv.org/abs/1712.09043

Deep Reinforcement Learning for List-wise Recommendations

• intro: Michigan State University & Data Science Lab
• arxiv: https://arxiv.org/abs/1801.00209

Recommendations with Negative Feedback via Pairwise Deep Reinforcement Learning

• intro: Michigan State University & JD.com
• arxiv: https://arxiv.org/abs/1802.06501

Personalized Top-N Sequential Recommendation via Convolutional Sequence Embedding

• intro: WSDM 2018. Simon Fraser University
• arxiv: https://arxiv.org/abs/1809.07426
• github(Matlab+MatcConvNet): https://github.com/graytowne/caser

Slides

Deep learning for music recommendation

• sldies: http://pan.baidu.com/s/1skriMJj

Deep learning for music recommendation and generation

• slides: https://docs.google.com/presentation/d/1AIotiiAp_528R90ll8j-Kc2EsRk2Oxc1poRgPXnEH8Y/edit
• mirror: https://pan.baidu.com/s/1czQQNO

Blogs

Recommending music on Spotify with deep learning

http://benanne.github.io/2014/08/05/spotify-cnns.html

Generating Recommendations at Amazon Scale with Apache Spark and Amazon DSSTNE

http://blogs.aws.amazon.com/bigdata/post/TxGEL8IJ0CAXTK/Generating-Recommendations-at-Amazon-Scale-with-Apache-Spark-and-Amazon-DSSTNE

Recommending movies with deep learning

• blog: http://blog.richardweiss.org/2016/09/25/movie-embeddings.html
• ipn: https://github.com/ririw/ririw.github.io/blob/master/assets/Recommending%20movies.ipynb

Deep Learning Helps iHeartRadio Personalize Music Recommendations

• blog: https://news.developer.nvidia.com/deep-learning-helps-iheartradio-personalize-music-recommendations/

Applying deep learning to Related Pins

• intro: Pinterest
• blog: https://engineering.pinterest.com/blog/applying-deep-learning-related-pins

Recommendation System Algorithms: Main existing recommendation engines and how they work

https://blog.statsbot.co/recommendation-system-algorithms-ba67f39ac9a3

Building a Music Recommender with Deep Learning

• intro: Music recommender using deep learning with Keras and TensorFlow
• blog: http://mattmurray.net/building-a-music-recommender-with-deep-learning/
• github: https://github.com/mattmurray/music_recommender

Projects

NNRec: Neural models for Collaborative Filtering

• intro: Source code for, AutoRec, an autoencoder based model for collaborative filtering. This package also includes implementation of RBM based collaborative filtering model(RBM-CF).
• github: https://github.com/mesuvash/NNRec

Deep learning recommend system with TensorFlow

• intro: a general project to walk through the proceses of using TensorFlow
• github: https://github.com/tobegit3hub/deep_recommend_system

Deep Learning Recommender System

• github: https://github.com/freegraphics/MIDS

Keras Implementation of Recommender Systems

https://github.com/sonyisme/keras-recommendation

Videos

Deep Learning for Recommender Systems

• youtube: https://www.youtube.com/watch?v=KZ7bcfYGuxw

Using MXNet for Recommendation Modeling at Scale

• youtube: https://www.youtube.com/watch?v=cftJAuwKWkA
• mirror: https://pan.baidu.com/s/1kVsdrmR

Resources

Recommender Systems with Deep Learning

https://amundtveit.com/2016/11/20/recommender-systems-with-deep-learning/

Deep-Learning-for-Recommendation-Systems

• intro: This repository contains Deep Learning based articles , paper and repositories for Recommender Systems
• github: https://github.com/robi56/Deep-Learning-for-Recommendation-Systems