Deep Learning in Recommender System
01-26
深度學習技術在計算機視覺、語音、自然語言處理方面已經相對成熟,工業界已經開始關注深度學習在推薦系統領域的應用。近三年KDD/WSDM/RecSys等會議出現越來越多的相關論文,這裡備忘深度學習&推薦系統相關的論文列表,供同行追蹤查閱。
(註:這個列表會一直更新,歡迎在評論中補充~)
2014年以前
- Salakhutdinov, Restricted Boltzmann Machines for Collaborative Filtering, ICML 2007 (Netflix Prize,SVD++,模型遇到瓶頸,用RBM改進協同過濾)
- Recommending Music on Spotify with Deep Learning(Spotify音樂推薦)
- Recurrent Neural Networks for Collaborative Filtering (Spotify音樂推薦,CF+DL)
- Oord et al., Deep Content-based Music Recommendation, NIPS 2013(用DL提取內容特徵,改進音樂推薦)
- Xinxi Wang et al., Improving Content-based and Hybrid Music Recommendation Using Deep Learning, ACM MM 2014 (用DL提取內容特徵,改進音樂推薦)
- Session-based Recommendations with Recurrent Neural Networks(Netflix研究員)
- A Multi-View Deep Learning Approach for Cross Domain User Modeling in Recommendation Systems, WWW 2015(微軟,新聞推薦領域使用DL的心得)
- A Deep Embedding Model for Co-occurrence Learning
- The Recommender Problem Revisited, KDD 2014 (推薦系統資深專家Netflix XavierAmatriain的分享,後來在RecSys多次分享Lessons Learned from Building Real--Life Recommender Systems)
2015年
- Sedhain et al., Autorec: Autoencoders Meet Collaborative Filtering, WWW 2015(用DL無監督Autoencoder改進Item-based CF,預測user對item的評分)
- Hao Wang & Naiyan Wang, Collaborative Deep Learning for Recommender Systems, KDD 2015(用DL改進協同過濾)
- A Deep Matrix Factorization Method for Learning Attribute Representations
2016年
- Yao Wu et al., Collaborative Denoising Autoencoders for Top-n Recommender Systems, WSDM 2016
- Yin Zheng et al., A Neural Autoregressive Approach to Collaborative Filtering, ICML 2016(Hulu團隊提出用於CF的神經自回歸架構,受啟發於基於CF的RBM和NADE,神經自回歸分布估計器,在Netflix數據集上取得SOTA結果)
- Yin Zheng et al., A Deep and Autoregressive Approach for Topic Modeling of Multimodal Data, IEEE Trans on PAMI 2016 (同樣來自Hulu團隊的工作)
- Junxuan Chen et al., Deep CTR Prediction in Display Advertising, ACM MM 2016(一個同時考慮圖像特徵和顯示廣告的特徵的深度學習網路)
- Covington et al., Deep Neural Networks for YouTube Recommendation, Recsys 2016(Google Youtube視頻推薦系統,two-stage的深度學習框架)
- Kim et al., Convolutional Matrix Factorization for Document Context-Aware Recommendation, Recsys 2016 (基於PMF和CNN做文檔推薦)
- Wide & Deep Learning for Recommender Systems, 2016(Google PlayApp推薦系統,著名的Wide & Deep模型,融合LR+DNN)
- Personal Recommendation Using Deep Recurrent Neural Networks in NetEase, 2016(網易考拉基於RNN和FNN的商品推薦)
- Deep Learning over Multi-field Categorical Data - A Case Study on User Response Prediction, ECIR 2016(提出FNN:預訓練FM得到組合特徵,再使用DNN做CTR預估)
- Product-based Neural Networks for User Response Prediction, CoRR 2016(提出PNN,在embedding層和全連接層中引入一層product層來學習組合特徵)
2017年
- Lei Zheng et al., Joint Deep Modeling of Users and Items Using Reviews forRecommendation, WSDM 2017.
- Chao-Yuan Wu & Alex Smola, Recurrent Recommender Networks, WSDM 2017
- A Hybrid Collaborative Filtering Model with Deep Structure for Recommender Systems, AAAI 2017(攜程提出混合協同過濾和深度AE結構)
- DeepFM: A Factorization-Machine based Neural Network for CTR Prediction, IJCAI 2017(華為諾亞方舟,受D&W啟發,提出FM+DNN融合模型,用於手機應用市場的CTR預估)
- Deep Matrix Factorization Models for Recommender Systems, IJCAI 2017(南京大學,用DNN改進傳統MF模型,2015有篇DeepMF學習屬性表達的文章)
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