This post recorded some recent researches in Recommender Systems specially associating with Deep Learning which were helpful for my own research.
- Deep Neural Networks for YouTube Recommendations(RecSys 2016)
- AutoRec: Autoencoders Meet Collaborative Filtering(WWW 2015)
- Hybrid Recommender System based on Autoencoders(2016)
- A Hybrid Collaborative Filtering Model with Deep Structure for Recommender Systems(2017,AAAI)
- Neural Collaborative Filtering(WWW 2017)
- Collaborative Deep Learning for Recommender Systems(KDD 2015)
- Wide & Deep Learning for Recommender Systems(DLRS 2016)
- Restricted Boltzmann machines for collaborative filtering(ICML 2007)
- Deep Collaborative Filtering: Deep Learning 技術の推薦システムへの応用(人工知能学会全国大会論文集 2014)
- Neural Collaborative Filtering(WWW 2017)
- Collaborative recurrent autoencoder: recommend while learning to fill in the blanks(NIPS 2016)
- Relational stacked denoising autoencoder for tag recommendation(AAA1 2015)
- Recurrent Recommender Networks(WSDM 2017)
- Deep Collaborative Filtering via Marginalized Denoising Auto-encoder(CIKM 2015)
- Autoencoder-Based Collaborative Filtering(ICONIP 2014)
- Collaborative Denoising Auto-Encoders for Top-N Recommender Systems(WSDM 2016)
- 多様性の導入による推薦システムにおけるユーザ体験向上の試み(自然言語処理 2017)
- Collaborative Knowledge Base Embedding for Recommender Systems(KDD 2016)
- Collaborative Filtering and Deep Learning Based Hybrid Recommendation for Cold Start Problem
- Latent Context-Aware Recommender Systems(RecSys 2015)
- Learning Distributed Representations from Reviews for Collaborative Filtering(RecSys 2015)
- Ask the GRU: Multi-task Learning for Deep Text Recommendations(RecSys 2016)
- Convolutional Matrix Factorization for Document Context-Aware Recommendation(RecSys 2016)
- Keynote: Deep learning for audio-based music recommendationDLRS 2016
- Hybrid Collaborative Filtering with Neural Networks(HAL 2016)
- Collaborative Filtering with Stacked Denoising AutoEncoders and Sparse Inputs(NIPS 2015)
- A Multi-View Deep Learning Approach for Cross Domain User Modeling in Recommendation Systems(WWW 2015)
- Dynamic Intention-Aware Recommendation System(ACM 2017)
- node2vec: Scalable Feature Learning for Networks(KDD 2016)