| layout | category | title | date |
|---|---|---|---|
post |
deep_learning |
Recommendation System |
2015-10-09 |
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
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
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
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
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
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
Deep learning for music recommendation
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
Recommending music on Spotify with deep learning
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
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
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
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
Keras Implementation of Recommender Systems
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
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