Neural Networks for Information Retrieval

Machine learning plays a role in many aspects of modern IR systems, and deep learning is applied in all of them. The fast pace of modern-day research has given rise to many approaches to many IR problems. The amount of information available can be overwhelming both for junior students and for experienced researchers looking for new research topics and directions. The aim of this full- day tutorial is to give a clear overview of current tried-and-trusted neural methods in IR and how they benefit IR.

[1]  Jiafeng Guo,et al.  Analysis of the Paragraph Vector Model for Information Retrieval , 2016, ICTIR.

[2]  Emine Yilmaz,et al.  Semi-supervised learning to rank with preference regularization , 2011, CIKM '11.

[3]  David Berthelot,et al.  WikiReading: A Novel Large-scale Language Understanding Task over Wikipedia , 2016, ACL.

[4]  W. Bruce Croft,et al.  Embedding-based Query Language Models , 2016, ICTIR.

[5]  Bhaskar Mitra,et al.  Reply With: Proactive Recommendation of Email Attachments , 2017, CIKM.

[6]  Tom Kenter,et al.  Byte-Level Machine Reading Across Morphologically Varied Languages , 2018, AAAI.

[7]  Alexandros Karatzoglou,et al.  Personalizing Session-based Recommendations with Hierarchical Recurrent Neural Networks , 2017, RecSys.

[8]  Oren Barkan,et al.  ITEM2VEC: Neural item embedding for collaborative filtering , 2016, 2016 IEEE 26th International Workshop on Machine Learning for Signal Processing (MLSP).

[9]  Dit-Yan Yeung,et al.  Collaborative Deep Learning for Recommender Systems , 2014, KDD.

[10]  Alex Graves,et al.  Neural Turing Machines , 2014, ArXiv.

[11]  Larry P. Heck,et al.  Learning deep structured semantic models for web search using clickthrough data , 2013, CIKM.

[12]  Edgar Meij,et al.  Utilizing Knowledge Bases in Text-centric Information Retrieval , 2016, ICTIR.

[13]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[14]  T. Landauer,et al.  Indexing by Latent Semantic Analysis , 1990 .

[15]  Bhaskar Mitra,et al.  A Dual Embedding Space Model for Document Ranking , 2016, ArXiv.

[16]  Yoshua Bengio,et al.  A Neural Probabilistic Language Model , 2003, J. Mach. Learn. Res..

[17]  Quoc V. Le,et al.  A Neural Conversational Model , 2015, ArXiv.

[18]  Andrew McCallum,et al.  Ask the GRU: Multi-task Learning for Deep Text Recommendations , 2016, RecSys.

[19]  Quoc V. Le,et al.  Distributed Representations of Sentences and Documents , 2014, ICML.

[20]  Lukás Burget,et al.  Recurrent neural network based language model , 2010, INTERSPEECH.

[21]  M. de Rijke,et al.  Siamese CBOW: Optimizing Word Embeddings for Sentence Representations , 2016, ACL.

[22]  Elena Smirnova,et al.  Meta-Prod2Vec: Product Embeddings Using Side-Information for Recommendation , 2016, RecSys.

[23]  W. Bruce Croft,et al.  LDA-based document models for ad-hoc retrieval , 2006, SIGIR.

[24]  M. de Rijke,et al.  Attentive Memory Networks: Efficient Machine Reading for Conversational Search , 2017, ArXiv.

[25]  Phil Blunsom,et al.  Teaching Machines to Read and Comprehend , 2015, NIPS.

[26]  Guillaume Lample,et al.  Neural Architectures for Named Entity Recognition , 2016, NAACL.

[27]  Hang Li,et al.  Semantic Matching in Search , 2014, SMIR@SIGIR.

[28]  David Grangier,et al.  Neural Text Generation from Structured Data with Application to the Biography Domain , 2016, EMNLP.

[29]  Quoc V. Le,et al.  Sequence to Sequence Learning with Neural Networks , 2014, NIPS.

[30]  Alessandro Moschitti,et al.  Learning to Rank Short Text Pairs with Convolutional Deep Neural Networks , 2015, SIGIR.

[31]  Nick Craswell,et al.  Query Expansion with Locally-Trained Word Embeddings , 2016, ACL.

[32]  Thomas Hofmann,et al.  Probabilistic Latent Semantic Indexing , 1999, SIGIR Forum.

[33]  Lukás Burget,et al.  Neural network based language models for highly inflective languages , 2009, 2009 IEEE International Conference on Acoustics, Speech and Signal Processing.

[34]  Jason Weston,et al.  Key-Value Memory Networks for Directly Reading Documents , 2016, EMNLP.

[35]  Yoshua Bengio,et al.  Generating Factoid Questions With Recurrent Neural Networks: The 30M Factoid Question-Answer Corpus , 2016, ACL.

[36]  Nick Craswell,et al.  Learning to Match using Local and Distributed Representations of Text for Web Search , 2016, WWW.

[37]  Benjamin Schrauwen,et al.  Deep content-based music recommendation , 2013, NIPS.

[38]  W. Bruce Croft,et al.  Semantic Matching by Non-Linear Word Transportation for Information Retrieval , 2016, CIKM.

[39]  Jason Weston,et al.  Learning End-to-End Goal-Oriented Dialog , 2016, ICLR.

[40]  M. de Rijke,et al.  An Introduction to Click Models for Web Search: SIGIR 2015 Tutorial , 2015, SIGIR.

[41]  M. de Rijke,et al.  Click Models for Web Search , 2015, Click Models for Web Search.

[42]  Jürgen Schmidhuber,et al.  Long Short-Term Memory , 1997, Neural Computation.

[43]  Marie-Francine Moens,et al.  Monolingual and Cross-Lingual Information Retrieval Models Based on (Bilingual) Word Embeddings , 2015, SIGIR.

[44]  Eric Nichols,et al.  Named Entity Recognition with Bidirectional LSTM-CNNs , 2015, TACL.

[45]  W. Bruce Croft,et al.  Improving Language Estimation with the Paragraph Vector Model for Ad-hoc Retrieval , 2016, SIGIR.

[46]  Yoshua Bengio,et al.  A Neural Knowledge Language Model , 2016, ArXiv.

[47]  Mandar Mitra,et al.  Word Embedding based Generalized Language Model for Information Retrieval , 2015, SIGIR.

[48]  Anton van den Hengel,et al.  Image-Based Recommendations on Styles and Substitutes , 2015, SIGIR.

[49]  M. de Rijke,et al.  Short Text Similarity with Word Embeddings , 2015, CIKM.

[50]  Matthew R. Walter,et al.  What to talk about and how? Selective Generation using LSTMs with Coarse-to-Fine Alignment , 2015, NAACL.

[51]  Jason Weston,et al.  Learning Structured Embeddings of Knowledge Bases , 2011, AAAI.

[52]  Hang Li,et al.  Convolutional Neural Network Architectures for Matching Natural Language Sentences , 2014, NIPS.

[53]  Zhiyuan Liu,et al.  Learning Entity and Relation Embeddings for Knowledge Graph Completion , 2015, AAAI.

[54]  Jaap Kamps,et al.  Avoiding Your Teacher's Mistakes: Training Neural Networks with Controlled Weak Supervision , 2017, ArXiv.

[55]  W. Bruce Croft,et al.  Estimating Embedding Vectors for Queries , 2016, ICTIR.

[56]  Jason Weston,et al.  Towards AI-Complete Question Answering: A Set of Prerequisite Toy Tasks , 2015, ICLR.

[57]  Nemanja Djuric,et al.  E-commerce in Your Inbox: Product Recommendations at Scale , 2015, KDD.

[58]  Geoffrey E. Hinton,et al.  Learning representations by back-propagating errors , 1986, Nature.

[59]  Maarten de Rijke,et al.  A Context-aware Time Model for Web Search , 2016, SIGIR.

[60]  Matt J. Kusner,et al.  From Word Embeddings To Document Distances , 2015, ICML.

[61]  Heng-Tze Cheng,et al.  Wide & Deep Learning for Recommender Systems , 2016, DLRS@RecSys.

[62]  Guido Zuccon,et al.  Integrating and Evaluating Neural Word Embeddings in Information Retrieval , 2015, ADCS.

[63]  M. de Rijke,et al.  A Neural Click Model for Web Search , 2016, WWW.

[64]  Jeffrey Dean,et al.  Efficient Estimation of Word Representations in Vector Space , 2013, ICLR.

[65]  Lukás Burget,et al.  Extensions of recurrent neural network language model , 2011, 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[66]  Stephen E. Robertson,et al.  Okapi at TREC-3 , 1994, TREC.

[67]  Fabrizio Silvestri,et al.  Context- and Content-aware Embeddings for Query Rewriting in Sponsored Search , 2015, SIGIR.

[68]  Stephen E. Robertson,et al.  GatfordCentre for Interactive Systems ResearchDepartment of Information , 1996 .

[69]  Zhiyuan Liu,et al.  Representation Learning for Measuring Entity Relatedness with Rich Information , 2015, IJCAI.

[70]  Yelong Shen,et al.  A Latent Semantic Model with Convolutional-Pooling Structure for Information Retrieval , 2014, CIKM.

[71]  Zhen Wang,et al.  Knowledge Graph Embedding by Translating on Hyperplanes , 2014, AAAI.

[72]  Michael I. Jordan,et al.  Latent Dirichlet Allocation , 2001, J. Mach. Learn. Res..

[73]  Yoshua Bengio,et al.  Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation , 2014, EMNLP.

[74]  W. Bruce Croft,et al.  Neural Ranking Models with Weak Supervision , 2017, SIGIR.

[75]  M. de Rijke,et al.  Learning Latent Vector Spaces for Product Search , 2016, CIKM.

[76]  Jason Weston,et al.  A unified architecture for natural language processing: deep neural networks with multitask learning , 2008, ICML '08.

[77]  Enrique Alfonseca,et al.  Learning to Attend, Copy, and Generate for Session-Based Query Suggestion , 2017, CIKM.

[78]  Marcel Worring,et al.  Unsupervised, Efficient and Semantic Expertise Retrieval , 2016, WWW.

[79]  Alexandros Karatzoglou,et al.  Session-based Recommendations with Recurrent Neural Networks , 2015, ICLR.

[80]  Jimmy Ba,et al.  Adam: A Method for Stochastic Optimization , 2014, ICLR.

[81]  Nitish Srivastava,et al.  Dropout: a simple way to prevent neural networks from overfitting , 2014, J. Mach. Learn. Res..

[82]  Jianfeng Gao,et al.  Deep Reinforcement Learning for Dialogue Generation , 2016, EMNLP.

[83]  Peter Young,et al.  Smart Reply: Automated Response Suggestion for Email , 2016, KDD.

[84]  Geoffrey E. Hinton,et al.  Semantic hashing , 2009, Int. J. Approx. Reason..

[85]  Yoshua Bengio,et al.  Generative Adversarial Nets , 2014, NIPS.

[86]  Yoshua Bengio,et al.  Neural Machine Translation by Jointly Learning to Align and Translate , 2014, ICLR.

[87]  M. de Rijke,et al.  Neural Vector Spaces for Unsupervised Information Retrieval , 2017, ACM Trans. Inf. Syst..

[88]  Hang Li,et al.  A Deep Architecture for Matching Short Texts , 2013, NIPS.

[89]  David Grangier,et al.  Generating Text from Structured Data with Application to the Biography Domain , 2016, ArXiv.