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Many recommender systems suffer from popularity bias: popular items are recommended frequently while less popular, niche products, are recommended rarely or not at all.
The long tail: Why the future of business is selling more for less
Chris Anderson. 2006 · 2006
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From niches to riches: Anatomy of the long tail
Erik Brynjolfsson, Yu Jeffrey Hu, and Michael D Smith. 2006 · 2006
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Trust-aware recommender systems. In Proceedings of the 2007 ACM conference on Recommender systems
Paolo Massa and Paolo Avesani. 2007 · 2007
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From hits to niches?: or how popular artists can bias music recommendation and discovery. In Proceedings of the 2nd KDD Workshop on Large-Scale Recommender Systems and the Netflix Prize Competition
Òscar Celma and Pedro Cano. 2008 · 2008
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The long tail of recommender systems and how to leverage it. In Proceedings of the 2008 ACM conference on Recommender systems
Yoon-Joo Park and Alexander Tuzhilin. 2008 · 2008
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Avoiding monotony: improving the diversity of recommendation lists. In Proceedings of the 2008 ACM conference on Recommender systems
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Exploiting query reformulations for web search result diversification. In Proceedings of the 19th international conference on World wide web
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Solving the apparent diversity-accuracy dilemma of recommender systems
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Novelty and diversity metrics for recommender systems: choice, discovery and relevance. In Proceedings of International Workshop on Diversity in Document Retrieval (DDR)
Pablo Castells, Saúl Vargas, and Jun Wang. 2011 · 2011
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Improving aggregate recommendation diversity using ranking-based techniques
Gediminas Adomavicius and YoungOk Kwon. 2012 · 2012
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Explicit relevance models in intent-oriented information retrieval diversification. In Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval
Saúl Vargas, Pablo Castells, and David Vallet. 2012 · 2012
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Challenging the long tail recommendation
Hongzhi Yin, Bin Cui, Jing Li, Junjie Yao, and Chen Chen. 2012 · 2012
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Bursting your (filter) bubble: strategies for promoting diverse exposure. In Proceedings of the 2013 conference on Computer supported cooperative work companion
Educational Recommendation with Multiple Stakeholders. In Third International Workshop on Educational Recommender Systems
Robin Burke and Himan Abdollahpouri. 2016 · 2016
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Towards Multi-Stakeholder Utility Evaluation of Recommender Systems. In Workshop on Surprise, Opposition, and Obstruction in Adaptive and Personalized Systems, UMAP 2016
Robin D. Burke, Himan Abdollahpouri, Bamshad Mobasher, and Trinadh Gupta. 2016 · 2016
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Statistical biases in Information Retrieval metrics for recommender systems
Alejandro Bellogín, Pablo Castells, and Iván Cantador. 2017 · 2017
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A Clustering Approach for Personalizing Diversity in Collaborative Recommender Systems. In Proceedings of the 25th Conference on User Modeling, Adaptation and Personalization
Farzad Eskandanian, Bamshad Mobasher, and Robin Burke. 2017 · 2017
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Paul Resnick, R Kelly Garrett, Travis Kriplean, Sean A Munson, and Natalie Jomini Stroud. 2013 · 2013
Cited alongside, same era.
The MovieLens Datasets: History and Context
F Maxwell Harper and Joseph A Konstan. 2015 · 2015
Cited alongside, same era.
Search result diversification
Rodrygo LT Santos, Craig Macdonald, Iadh Ounis, et al · 2015
Cited alongside, same era.
Controlling Popularity Bias in Learning-to-Rank Recommendation. In Proceedings of the Eleventh ACM Conference on Recommender Systems
Himan Abdollahpouri, Robin Burke, and Bamshad Mobasher. 2017a
Cited in the paper.
Recommender systems as multi-stakeholder environments. In Proceedings of the 25th Conference on User Modeling, Adaptation and Personalization (UMAP2017)
Himan Abdollahpouri, Robin Burke, and Bamshad Mobasher. 2017b
Cited in the paper.
Weiwen Liu and Robin Burke. 2018 · 2018
Later among the works it cites.
Intent-aware Item-based Collaborative Filtering for Personalised Diversification. In Proceedings of the 26th Conference on User Modeling, Adaptation and Personalization
Jacek Wasilewski and Neil Hurley. 2018 · 2018
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Beyond Personalization: Research Directions in Multistakeholder Recommendation
Himan Abdollahpouri, Gediminas Adomavicius, Robin Burke, Ido Guy, Dietmar Jannach, Toshihiro Kamishima, Jan Krasnodebski, and Luiz Pizzato. 2019 · 2019
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