Diversifying search results

By Rakesh Agrawal, Sreenivas Gollapudi, Alan Halverson, Samuel Ieong. 2009

1,160 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Information Systems

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In Computer Vision and Pattern Recognition

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Citations in the corpus

Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI (2019, neutral): “Likewise, certain AI problems (such as content recommendation or information retrieval) also aim at producing diverse recommendations rather than highly-scoring yet similar results [405,406] .”
Melding the Data-Decisions Pipeline: Decision-Focused Learning for Combinatorial Optimization (2018, methodology): “Such formulations have been used across recommendation systems (Ashkan et al. 2015), text summarization (Takamura and Okumura 2009), web search (Agrawal et al. 2009) and image segmentation (Prasad, Jegelka, and Batra 2014).”
Designing Fair Ranking Schemes (2017, background): “We represent an item t as a d -long vector of scoring attributes, { t [1] , t [2] , . . . , t [ d ] } .”
Test Collection Based Evaluation of Information Retrieval Systems (2010, background): “[2] pointed out that some of the sub-topics of a query could be more popular or important than others.”
Overview of the TREC 2009 Web Track (2009, methodology): “[1] treat each subtopic as a distinct interpretation of the associated query.”
Search result diversification (2010, methodology): “For example, [1] considers typical web search and, given a query q and a taxonomy C of independent information categories, aims at retrieving k documents that cover many interpretations of q, especially interpretations that are considered important.”
Novelty and Diversity in Recommender Systems (2015, methodology): “The diversity problem is being stated in the IR community as an issue of query ambiguity and underspecification, which is formulated in terms of query aspects, interpretations, or similar notions [2,9].”
Detecting duplicate web documents using clickthrough data (2011, background): “…Emine Yilmaz Microsoft Microsoft Research Microsoft 840 Cambie Street 1 Microsoft Way 7 J J Thomson Ave Vancouver, BC, CanadaRedmond, WA, USACambridge, UK .liprad@microsoft.com pauben@microsoft.com eminey@microsoft.com ABSTRACT The web contains many duplicate and near-duplicate documents.”
Towards Large-Scale Collaborative Planning: Answering High-Level Search Queries Using Human Computation (2011, result): “Finally, our work is related to research on diversifying search results (Santos, Macdonald, and Ounis 2010; Clarke et al. 2008; Agrawal et al. 2009; Gollapudi and Sharma 2009; Chen and Karger 2006) as a way to better answer faceted queries (e.g., " wedding planning " ), which are queries that…”
Answer Interaction in Non-factoid Question Answering Systems (2019, background): “Modern search engines have paid attention to search results diversification [3, 21] and heterogeneous content presentation [29].”
Fast Greedy MAP Inference for Determinantal Point Process to Improve Recommendation Diversity (2017, background): “Some works proposed to define the similarity metric based on the taxonomy information [52, 2, 12, 45, 4, 44].”
Diversity in Big Data: A Review (2017, neutral): “In Web search, 7,9 poor result diversity leads to poor user engagement, caters only to the most common information needs and fails to address less typical ones, and limits exposure of the ‘‘long tail’’ of results.”
Diversity, Serendipity, Novelty, and Coverage (2016, background): “This can be achieved by optimizing the diversity of the document list, which can be measured in terms of features (e.g., document types, information facts, topics) that the documents in the list possess [Carbonell and Goldstein 1998; Clarke et al. 2008; Wang and Zhu 2009; Agrawal et al. 2009].”
Assessor Differences and User Preferences in Tweet Timeline Generation (2015, background): “These notions are also reﬂected in variants of ad hoc retrieval such as aspect retrieval [21] (or, alternatively, facet or sub-topic retrieval [34]) and result diversiﬁcation [1, 28], although less explicitly.”
Learning to rank for information retrieval (2009, neutral): “In: Proceedings of Swedish Conference on Connectionism (SCC 1995) (1995) 5. Bianchini, M., Gori, M., Scarselli, F.: Inside pagerank. ACM Transactions on Internet Technologies 5(1), 92–128 (2005) 6. Boldi, P., Santini, M., Vigna, S.: Pagerank as a function of the damping factor. In: Proceedings of th”
Expected reciprocal rank for graded relevance (2009, background): “…that a user stops at rank r is: And the diversity extension of ERR can be written as: Interestingly a similar equation to (6) has been derived in [1], but for the purpose of ﬁnding a diverse set of results, not for evaluation (their evaluation is based on an expected DCG which, in our opinion,…”