An axiomatic approach for result diversification

By Sreenivas Gollapudi, Aneesh Sharma. The Web Conference, 2009

475 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Information Systems

View Paper: https://www.semanticscholar.org/paper/10486f0102297696f51da9f8f7c4ded4c0c49fb3

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Citations in the corpus

Diversity in Big Data: A Review (2017, unknown): “For example, it has been shown that when div is a metric, there are efficient approximation algorithms for the diversification problem.(36) To showcase the flexibility of distance-based measures, we present two examples.”
Towards Large-Scale Collaborative Planning: Answering High-Level Search Queries Using Human Computation (2011, background): “Diversification of search results can be seen as a bi-criterion optimization problem (Gollapudi and Sharma 2009) that aims to maximize relevance and diversity in the search results , while minimizing redundancy.”
Detecting duplicate web documents using clickthrough data (2011, background): “In particular, this research is often motivated by ambiguity often seen in queries issued to web search engines, with a variety of techniques being developed to assess how well a particular ranking or set of documents satises the possible intents for a given query (e.g., [15, 20 , 35]).”
In Defense of Minhash over Simhash (2014, background): “…in the search industry, with numerous applications, for example, large-sale machine learning systems [23, 21], Web spam [29, 18], content matching for online advertising [25], compressing social networks [9], advertising diversiﬁcation [13], graph sampling [24], Web graph compression [5], etc.”
Ranking with Fairness Constraints (2017, background): “Further, [GS09] showed that no single diversiﬁcation function can satisfy a set of natural axioms that one would want any fair ranking to have.”
b-Bit Minwise Hashing for Estimating Three-Way Similarities (2010, methodology): “These techniques have been successfully applied to many tasks in machine learning, databases, data mining, and information retrieval [18, 36, 11, 22, 16, 39, 28, 41, 27, 5, 2, 37, 7, 24, 21].”
Exploiting query reformulations for web search result diversification (2010, background): “Search result diversification can be characterised as a bicriterion optimisation problem, in which one seeks to maximise the overall relevance of a document ranking to multiple query aspects, while minimising its redundancy with respect to these aspects [14].”
Search result diversification (2010, methodology): “[7] also employs another greedy variation, first presented in [9] as a solution to the p-dispersion problem, in which, at each iteration, the two remaining items with the largest pair-wise distance are added to S.”