A list of recent papers regarding deep reinforcement learning.

Any suggestions and pull requests are welcome.

• Model-Free Episodic Control, C. Blundell et al., arXiv, 2016.ss
• Bridging the Lexical Chasm: Statistical Approaches to Answer-Finding, Adam Berger et al., SIGIR, 2000
• Click-through-based Translation Models for Web Search: from Word Models to Phrase Models, Jianfeng Gao et al, CIKM, 2010
• Clickthrough-based latent semantic models for web search, Jianfeng Gao et al, SIGIR, 2011
• Imrpoved Ad Relevance in Sponsored Search, Dustin Hillard et al, WSDM, 2010
• Exploring web scale language models for search query processing, Jian Huang et al, WWW, 2010
• Translation Retrieval Model for Cross Lingual Information Retrieval,Ea-Ee Jan et al, AIRS, 2010
• Title Language Model for Information Retrieval, Rong Jin et al, SIGIR, 2002
• Estimation of Statistical Translation Models based on Mutual Information for Ad Hoc Information Retrieval, Maryan Karimzadehgan et al, SIGIR, 2010
• Polylingual topic models, David Mimno et al, EMNLP, 2009
• Information Retrieval as Statistical Translation, et al, SIGIR, 1999
• Qusi-Synchronous Dependence Model for Information Retrieval, Jae-Hyun Park et al, CIKM, 2011
• Query Rewritting Using Monolingual Statistical Machine Translation, Stefan Riezler et al, ACL, 2010
• A cross-lingual Framework for Monolingual Biomedical Information Retrieval, Dolf Trieschnigg et al, CIKM, 2010
• Combining Probabilistic and Translation-based Models for Information Retrieval based on Word Sense Annotations, Elisabeth Wolf et al, CLEF Workshop, 2009
• Canonical correlation analysis: An overview with application to learning methods, D.R. Hardoon et al, Neural Computation, 2004
• lickthrough-based latent semantic models for web search, Jianfeng Gao et al, SIGIR, 2011
• Learning Query and Document Similarities from Click-through Bipartite Graph with Metadata, Wei Wu et al, Microsoft Research Technical Report, 2011
• Relevance Ranking Using Kernels, Jun Xu et al, AIRS, 2010
• Psychology of Learning and Motivation: Advances in Research and Theory, Brian H. Ross et al, Elsevier, 2002

• Fast matrix factorization for online recommendation with implicit feedback,Xiangnan He et al, SIGIR, 2016
• Advances in collaborative filtering. Recommender systems handbook, Yehuda Koren et al, MA, 2015
• Fism: factored item similarity models for top-n recommender systems, Santosh Kabbur et al, KDD, 2013
• Factorization meets the neighborhood: a multifaceted collaborative filtering model, Yehuda Koren et al, KDD, 2018
• Factorization machines, Steffen Rendle et al, ICDM, 2010
• Collaborative filtering with temporal dynamics, Yehuda Koren et al, Communications of the ACM, 2010
• Pairwise interaction tensor factorization for personalized tag recommendation, Steffen Rendle et al, WSDM, 2010
• A generic coordinate descent framework for learning from implicit feedback, Immanuel Bayer et al, WWW, 2017
• Neural collaborative filtering, Xiangnan He et al, WWW, 2017
• BPR: Bayesian personalized ranking from implicit feedback, Steffen Rendle et al, UAI, 2009

• oogle turning its lucrative web search over to ai machines, Clark J et al, Bloomberg Technology, 2015
• AI is transforming Google search, Metz C et al, WIRED Magazine, 2016
• Learning deep structured semantic models for web search using clickthrough data, Huang P S et al, ACM international conference on Conference on information & knowledge management, 2013
• A latent semantic model with convolutional-pooling structure for information retrieval, Hu B et al, ACM International Conference on Conference on Information and Knowledge Management, 2014
• Convolutional neural network architectures for matching natural language sentences, Z, Li H et al, * Advances in neural information processing systems *, 2014
• Convolutional Neural Tensor Network Architecture for Community-Based Question Answering, Qiu X et al, IJCAI, 2015
• Deep sentence embedding using long short-term memory networks: Analysis and application to information retrieval, Palangi H et al, TASLP, 2016
• Multigrancnn: An architecture for general matching of text chunks on multiple levels of granularity, Yin W et al, International Joint Conference on Natural Language Processin, 2015
• Dynamic pooling and unfolding recursive autoencoders for paraphrase detection, Socher R et al, Advances in neural information processing systems, 2011
• A Deep Architecture for Semantic Matching with Multiple Positional Sentence Representations, Wan S et al, AAAI, 2016
• Text Matching as Image Recognition, Pang L et al, AAAI, 2016
• MatchSRNN: modeling the recursive matching structure with spatial RNN, Shengxian Wan et al, IJCAI, 2016
• A Decomposable Attention Model for Natural Language Inference, Ankur P. Parikh et al, EMNLP, 2016
• Convolutional Neural Networks for Soft-Matching N-Grams in Ad-hoc Search, Zhuyun Dai et al, WSDM, 2018
• End-to-End Neural Ad-hoc Ranking with Kernel Pooling, Chenyan Xiong et al, SIGIR, 2017

• Deep neural networks for youtube recommendations, Paul Covington et al, Recsys, 2016
• item silk road: Recommending items from information domains to social users, Xiang Wang et al, SIGIR, 2017
• Deep matrix factorization models for recommender systems, Hong-Jian Xue et al, IJCAI, 2017
• Autorec: Autoencoders meet collaborative filtering, Suvash Sedhain et al, WWW, 2015
• Collaborative denoising autoencoders for top-n recommender systems, Yao Wu et al, WSDM, 2016
• Deep collaborative filtering via marginalized denoising autoencoder, Sheng Li et al, CIKM, 2015
• Learning image and user features for recommendation in social networks, Xue Geng et al, ICCV, 2015
• Attentive collaborative filtering: Multimedia recommendation with item-and component-level attention, Jingyuan Chen et al, SIGIR, 2017
• Collaborative knowledge base embedding for recommender systems, Fuzheng Zhang et al, KDD, 2016
• Neural collaborative filtering, Xiangnan He et al, WWW, 2017
• A Neural Collaborative Filtering Model with Interaction-based Neighborhood, et al, CIKM, 2017
• Out Product- based Neural Collaborative Filtering, Xiangnan He et al, IJCAI, 2018
• Translation-based Recommendation, Ruining He et al, Recsys, 2017
• Latent Relational Metric Learning via Memory-based Attention for Collaborative Ranking, Yi Tay et al, WWW, 2018
• Wide & deep learning for recommender systems, Heng-Tze Cheng et al, DLRS, 2016
• Deep crossing: Web-scale modeling without manually crafted combinatorial features, Ying Shan et al, KDD, 2016
• Neural factorization machines for sparse predictive analytics, Xiangnan He et al, SIGIR, 2017
• Attentional factorization machines: Learning the weight of feature interactions via attention networks, Jun Xiao et al, IJCAI, 2017
• B-CENT: Gradient Boosted Categorical Embedding and Numerical Trees, Qian Zhao et al, WWW, 2017
• Deep embedding forest: Forest-based serving with deep embedding features, Jie Zhu et al, KDD, 2017
• TEM: Tree-enhanced Embedding Model for Explainable Recommendation, Xiang Wang et al, WWW, 2018