Affiliation:
1. Sapienza, University of Rome, Italy
2. Yahoo Labs, Barcelona, Spain
3. University of Warsaw, Poland
Abstract
We design algorithms that, given a collection of documents and a distribution over user queries, return a small subset of the document collection in such a way that we can efficiently provide high-quality answers to user queries using only the selected subset. This approach has applications when space is a constraint or when the query-processing time increases significantly with the size of the collection. We study our algorithms through the lens of stochastic analysis and prove that even though they use only a small fraction of the entire collection, they can provide answers to most user queries, achieving a performance close to the optimal. To complement our theoretical findings, we experimentally show the versatility of our approach by considering two important cases in the context of Web search. In the first case, we favor the retrieval of documents that are relevant to the query, whereas in the second case we aim for document diversification. Both the theoretical and the experimental analysis provide strong evidence of the potential value of query covering in diverse application scenarios.
Funder
EU FET projects MULTIPLEX 317532 and SIMPOL 610704
EU FP7 project 255403-SNAPS
PRIN 2008 research projects COGENT (COmputational and GamE-theoretic aspects of uncoordinated NeTworks) and Mad Web (Models, Algorithms and Data structures for the Web and other behavioral networks)
EU ERC StG project PAAl 259515
Italian Ministry of University and Research
Google Focused Award “Algorithms for Large-Scale Data Analysis”
Publisher
Association for Computing Machinery (ACM)
Subject
Computer Science Applications,General Business, Management and Accounting,Information Systems
Cited by
7 articles.
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