Incorporating query constraints for autoencoder enhanced ranking
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Hongfei Lin | Bo Xu | Yuan Lin | Kan Xu | Hongfei Lin | Bo Xu | Yuan Lin | Kan Xu
[1] Yelong Shen,et al. Learning semantic representations using convolutional neural networks for web search , 2014, WWW.
[2] Yoshua. Bengio,et al. Learning Deep Architectures for AI , 2007, Found. Trends Mach. Learn..
[3] J. Friedman. Greedy function approximation: A gradient boosting machine. , 2001 .
[4] Dit-Yan Yeung,et al. Relational Stacked Denoising Autoencoder for Tag Recommendation , 2015, AAAI.
[5] W. Bruce Croft,et al. Learning a Deep Listwise Context Model for Ranking Refinement , 2018, SIGIR.
[6] Alessandro Moschitti,et al. Learning to Rank Short Text Pairs with Convolutional Deep Neural Networks , 2015, SIGIR.
[7] Yoram Singer,et al. An Efficient Boosting Algorithm for Combining Preferences by , 2013 .
[8] Huazheng Wang,et al. Efficient Exploration of Gradient Space for Online Learning to Rank , 2018, SIGIR.
[9] Larry P. Heck,et al. Learning deep structured semantic models for web search using clickthrough data , 2013, CIKM.
[10] Gregory N. Hullender,et al. Learning to rank using gradient descent , 2005, ICML.
[11] Tao Qin,et al. LETOR: A benchmark collection for research on learning to rank for information retrieval , 2010, Information Retrieval.
[12] Salvatore Orlando,et al. Selective Gradient Boosting for Effective Learning to Rank , 2018, SIGIR.
[13] W. Bruce Croft,et al. A Deep Relevance Matching Model for Ad-hoc Retrieval , 2016, CIKM.
[14] Djoerd Hiemstra,et al. A cross-benchmark comparison of 87 learning to rank methods , 2015, Inf. Process. Manag..
[15] M. de Rijke,et al. Multileave Gradient Descent for Fast Online Learning to Rank , 2016, WSDM.
[16] Min Yang,et al. Investigating Deep Reinforcement Learning Techniques in Personalized Dialogue Generation , 2018, SDM.
[17] Nick Craswell,et al. Learning to Match using Local and Distributed Representations of Text for Web Search , 2016, WWW.
[18] Jun Xu,et al. Modeling Diverse Relevance Patterns in Ad-hoc Retrieval , 2018, SIGIR.
[19] Hongbo Deng,et al. Ranking Relevance in Yahoo Search , 2016, KDD.
[20] Daniel Jurafsky,et al. A Hierarchical Neural Autoencoder for Paragraphs and Documents , 2015, ACL.
[21] Tie-Yan Liu,et al. Learning to rank: from pairwise approach to listwise approach , 2007, ICML '07.
[22] Min Yang,et al. Feature-enhanced attention network for target-dependent sentiment classification , 2018, Neurocomputing.
[23] Peng Zhang,et al. IRGAN: A Minimax Game for Unifying Generative and Discriminative Information Retrieval Models , 2017, SIGIR.
[24] Kyomin Jung,et al. Learning to Rank Question-Answer Pairs Using Hierarchical Recurrent Encoder with Latent Topic Clustering , 2017, NAACL.
[25] Hui Xiong,et al. Representation Learning via Semi-Supervised Autoencoder for Multi-task Learning , 2015, 2015 IEEE International Conference on Data Mining.
[26] Xiaoyu Du,et al. Adversarial Personalized Ranking for Recommendation , 2018, SIGIR.
[27] Thorsten Joachims,et al. Unbiased Learning-to-Rank with Biased Feedback , 2016, WSDM.
[28] Zhongfei Zhang,et al. Semisupervised Autoencoder for Sentiment Analysis , 2015, AAAI.
[29] Huan Liu,et al. Turning Clicks into Purchases: Revenue Optimization for Product Search in E-Commerce , 2018, SIGIR.
[30] Christian S. Jensen,et al. Efficient Online Summarization of Large-Scale Dynamic Networks , 2016, IEEE Transactions on Knowledge and Data Engineering.
[31] Scott Sanner,et al. AutoRec: Autoencoders Meet Collaborative Filtering , 2015, WWW.
[32] Byron J. Gao,et al. Learning to rank for hybrid recommendation , 2012, CIKM.
[33] Nicholas Jing Yuan,et al. Representation Learning with Pair-wise Constraints for Collaborative Ranking , 2017, WSDM.
[34] Bin Wang,et al. Learning to rank for question routing in community question answering , 2013, CIKM.
[35] Christopher J. C. Burges,et al. From RankNet to LambdaRank to LambdaMART: An Overview , 2010 .
[36] Hongfei Lin,et al. Learning to Rank with Query-level Semi-supervised Autoencoders , 2017, CIKM.
[37] Yoshua Bengio,et al. Extracting and composing robust features with denoising autoencoders , 2008, ICML '08.
[38] Christian S. Jensen,et al. Integrating non-spatial preferences into spatial location queries , 2014, SSDBM '14.
[39] Siyuan Liu,et al. Heterogeneous anomaly detection in social diffusion with discriminative feature discovery , 2018, Inf. Sci..
[40] Tao Qin,et al. Introducing LETOR 4.0 Datasets , 2013, ArXiv.
[41] Tie-Yan Liu. Learning to Rank for Information Retrieval , 2009, Found. Trends Inf. Retr..
[42] Quoc V. Le,et al. Abstract , 2003, Appetite.
[43] Inderjit S. Dhillon,et al. Clustering with Bregman Divergences , 2005, J. Mach. Learn. Res..
[44] Xueqi Cheng,et al. What makes data robust: a data analysis in learning to rank , 2014, SIGIR.
[45] Wei Zeng,et al. From Greedy Selection to Exploratory Decision-Making: Diverse Ranking with Policy-Value Networks , 2018, SIGIR.