Deep Learning for Information Retrieval and Learning to Rank

This posting is about Deep Learning for Information Retrieval and Learning to Rank (i.e. of interest if developing search engines). The posting is complemented by the posting Deep Learning for Question Answering. To get started I recommend checking out Jianfeng Gao‘s (Deep Learning Technology Center at Microsoft Research) presentation Deep Learning for Web Search and Natural Language Processing.

Of partial relevance is the posting Deep Learning for Sentiment Analysis, the posting about Embedding for NLP with Deep Learning, the posting about Deep Learning for Natural Language Processing (ICLR 2017 discoveries), and the posting about Deep Learning for Recommender Systems

Best regards,
Amund Tveit

 

Year  Title Author
2016   Efficient Inference, Search and Evaluation for Latent Variable Models of Text with Applications to Information Retrieval and Machine Translation  K Krstovski
2016   Content-based Information Retrieval via Nearest Neighbor Search  Y Huang
2016   Information retrieval in instant messaging platforms using Recurrent Neural Networks  JHP Suorra
2016   Information Retrieval with Dimensionality Reduction using Deep Belief Networks  V Slot
2015   Deep Sentence Embedding Using the Long Short Term Memory Network: Analysis and Application to Information Retrieval  H Palangi, L Deng, Y Shen, J Gao, X He, J Chen
2014   A compositional hierarchical model for music information retrieval  M Pesek, A Leonardis, M Marolt
2014   Log-Bilinear Document Language Model for Ad-hoc Information Retrieval  X Tu, J Luo, B Li, T He
2016   Learning to rank chemical compounds based on their multiprotein activity using Random Forests  D Lesniak, M l Kowalik, P Kruk
2016   Multilevel Syntactic Parsing Based on Recursive Restricted Boltzmann Machines and Learning to Rank  J Xu, H Chen, S Zhou, B He
2016   Automatic Face Recognition Based On Learning to Rank for Image Quality Assessment  K Busa, G Tejaswi
2015   Application of Learning to Rank to protein remote homology detection  B Liu, J Chen, X Wang
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Recommender Systems with Deep Learning

Update: 2017-Feb-03 – launched new service – ai.amundtveit.com (navigation and search in papers). Try e.g. out its Collaborative Filtering and Recommender pages.


This blog post presents recent research in Recommender Systems (/collaborative filtering) with Deep Learning. To get started I recommend having a look at A Survey and Critique of Deep Learning in Recommender Systems.

If you are curious about oversight of Deep Learning topics, please consider subscribing to my Deep Learning Newsletter at the end of this blog post.

Best regards,
Amund Tveit


Recommender Systems with Deep Learning

  1. Improving Scalability of Personalized Recommendation Systems for Enterprise Knowledge Workers
    – Authors: C Verma, M Hart, S Bhatkar, A Parker (2016)
  2. Multi-modal learning for video recommendation based on mobile application usage
    – Authors: X Jia, A Wang, X Li, G Xun, W Xu, A Zhang (2016)
  3. Collaborative Filtering with Stacked Denoising AutoEncoders and Sparse Inputs
    – Authors: F Strub, J Mary (2016)
  4. Applying Visual User Interest Profiles for Recommendation and Personalisation
    – Authors: J Zhou, R Albatal, C Gurrin (2016)
  5. Comparative Deep Learning of Hybrid Representations for Image Recommendations
    – Authors: C Lei, D Liu, W Li, Zj Zha, H Li (2016)
  6. Tag-Aware Recommender Systems Based on Deep Neural Networks
    – Authors: Y Zuo, J Zeng, M Gong, L Jiao (2016)
  7. Quote Recommendation in Dialogue using Deep Neural Network
    – Authors: H Lee, Y Ahn, H Lee, S Ha, S Lee (2016)
  8. Toward Fashion-Brand Recommendation Systems Using Deep-Learning: Preliminary Analysis
    – Authors: Y Wakita, K Oku, K Kawagoe (2016)
  9. Word embedding based retrieval model for similar cases recommendation
    – Authors: Y Zhao, J Wang, F Wang (2016)
  10. ConTagNet: Exploiting User Context for Image Tag Recommendation
    – Authors: Ys Rawat, Ms Kankanhalli (2016)
  11. Wide & Deep Learning for Recommender Systems
    – Authors: Ht Cheng, L Koc, J Harmsen, T Shaked, T Chandra… (2016)
  12. On Deep Learning for Trust-Aware Recommendations in Social Networks.
    – Authors: S Deng, L Huang, G Xu, X Wu, Z Wu (2016)
  13. A Survey and Critique of Deep Learning on Recommender Systems
    – Authors: L Zheng (2016)
  14. Collaborative Filtering and Deep Learning Based Hybrid Recommendation for Cold Start Problem
    – Authors: J Wei, J He, K Chen, Y Zhou, Z Tang (2016)
  15. Collaborative Filtering and Deep Learning Based Recommendation System For Cold Start Items
    – Authors: J Wei, J He, K Chen, Y Zhou, Z Tang (2016)
  16. Deep Neural Networks for YouTube Recommendations
    – Authors: P Covington, J Adams, E Sargin (2016)
  17. Towards Latent Context-Aware Recommendation Systems
    – Authors: M Unger, A Bar, B Shapira, L Rokach (2016)
  18. Automatic Recommendation Technology for Learning Resources with Convolutional Neural Network
    – Authors: X Shen, B Yi, Z Zhang, J Shu, H Liu (2016)
  19. Tag-Aware Personalized Recommendation Using a Deep-Semantic Similarity Model with Negative Sampling
    – Authors: Z Xu, C Chen, T Lukasiewicz, Y Miao, X Meng (2016)
  20. Latent Factor Representations for Cold-Start Video Recommendation
    – Authors: S Roy, Sc Guntuku (2016)
  21. Convolutional Matrix Factorization for Document Context-Aware Recommendation
    – Authors: D Kim, C Park, J Oh, S Lee, H Yu (2016)
  22. Conversational Recommendation System with Unsupervised Learning
    – Authors: Y Sun, Y Zhang, Y Chen, R Jin (2016)
  23. RecSys’ 16 Workshop on Deep Learning for Recommender Systems (DLRS)
    – Authors: A Karatzoglou, B Hidasi, D Tikk, O Sar (2016, Workshop proceedings)
  24. Ask the GRU: Multi-task Learning for Deep Text Recommendations
    – Authors: T Bansal, D Belanger, A Mccallum (2016)
  25. Recurrent Coevolutionary Latent Feature Processes for Continuous-Time Recommendation
    – Authors: H Dai, Y Wang, R Trivedi, L Song (2016)
  26. Keynote: Deep learning for audio-based music recommendation
    – Authors: S Dieleman (2016)
  27. Tumblr Blog Recommendation with Boosted Inductive Matrix Completion
    – Authors: D Shin, S Cetintas, Kc Lee, Is Dhillon (2015)
  28. Deep Collaborative Filtering via Marginalized Denoising Auto-encoder
    – Authors: S Li, J Kawale, Y Fu (2015)
  29. Learning Image and User Features for Recommendation in Social Networks
    – Authors: X Geng, H Zhang, J Bian, Ts Chua (2015)
  30. UCT-Enhanced Deep Convolutional Neural Network for Move Recommendation in Go
    – Authors: S Paisarnsrisomsuk (2015)
  31. A Multi-View Deep Learning Approach for Cross Domain User Modeling in Recommendation Systems
    – Authors: A Elkahky, Y Song, X He (2015)
  32. It Takes Two to Tango: An Exploration of Domain Pairs for Cross-Domain Collaborative Filtering
    – Authors: S Sahebi, P Brusilovsky (2015)
  33. Latent Context-Aware Recommender Systems
    – Authors: M Unger (2015)
  34. Learning Distributed Representations from Reviews for Collaborative Filtering
    – Authors: A Almahairi, K Kastner, K Cho, A Courville (2015)
  35. A Collaborative Filtering Approach to Real-Time Hand Pose Estimation
    – Authors: C Choi, A Sinha, Jh Choi, S Jang, K Ramani (2015)
  36. Collaborative Deep Learning for Recommender Systems
    – Authors: H Wang, N Wang, Dy Yeung (2014)
  37. CARS2: Learning Context-aware Representations for Context-aware Recommendations
    – Authors: Y Shi, A Karatzoglou, L Baltrunas, M Larson, A Hanjalic (2014)
  38. Relational Stacked Denoising Autoencoder for Tag Recommendation
    – Authors: H Wang, X Shi, Dy Yeung (2014)

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