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Despite the somewhat different techniques used in developing search engines and recommender systems, they both follow the same goal: helping people to get the information they need at the right time.
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Neural Ranking Models with Weak Supervision. In SIGIR ’17
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Trapit Bansal, David Belanger, and Andrew McCallum. 2016 · 2016
Cited alongside, same era.
A Deep Relevance Matching Model for Ad-hoc Retrieval. In CIKM ’16
Jiafeng Guo, Yixing Fan, Qingyao Ai, and W. Bruce Croft. 2016 · 2016
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Ups and Downs: Modeling the Visual Evolution of Fashion Trends with One-Class Collaborative Filtering. In WWW ’16
Ruining He and Julian McAuley. 2016 · 2016
Cited alongside, same era.
Learning Latent Vector Spaces for Product Search. In CIKM ’16
Christophe Van Gysel, Maarten de Rijke, and Evangelos Kanoulas. 2016 · 2016
Cited alongside, same era.
Pseudo-Relevance Feedback Based on Matrix Factorization. In CIKM ’16
Hamed Zamani, Javid Dadashkarimi, Azadeh Shakery, and W. Bruce Croft. 2016 · 2016
Cited alongside, same era.
Neural Ranking Models with Multiple Document Fields. In WSDM ’18
Hamed Zamani, Bhaskar Mitra, Xia Song, Nick Craswell, and Saurabh Tiwary. 2018b
Cited in the paper.
Joint Representation Learning for Top-N Recommendation with Heterogeneous Information Sources. In CIKM ’17
Yongfeng Zhang, Qingyao Ai, Xu Chen, and W. Bruce Croft. 2017 · 2017
Later among the works it cites.
Current Challenges and Visions in Music Recommender Systems Research
Markus Schedl, Hamed Zamani, Ching-Wei Chen, Yashar Deldjoo, and Mehdi Elahi. 2018 · 2018
Closest in time.
Neural Query Performance Prediction Using Weak Supervision from Multiple Signals. In SIGIR ’18
Hamed Zamani, W. Bruce Croft, and J. Shane Culpepper. 2018 · 2018
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SIGIR 2018 Workshop on Learning from Limited or Noisy Data for Information Retrieval. In SIGIR ’18
Hamed Zamani, Mostafa Dehghani, Fernando Diaz, Hang Li, and Nick Craswell. 2018a · 2018
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A Language Model-based Framework for Multi-publisher Content-based Recommender Systems
Hamed Zamani and Azadeh Shakery. 2018 · 2018
Closest in time.