NDLI: Reranking search results for sparse queries

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Discovering missing click-through query language information for web search

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Simulating simple user behavior for system effectiveness evaluation

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Bayesian latent variable models for collaborative item rating prediction

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Discovering top-k teams of experts with/without a leader in social networks

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Scalable density-based subspace clustering

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Semi-indexing semi-structured data in tiny space

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Learning to target: what works for behavioral targeting

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Reranking search results for sparse queries

Content Provider	ACM Digital Library
Author	Aktolga, Elif Allan, James
Abstract	It is well known that clickthrough data can be used to improve the effectiveness of search results: broadly speaking, a query's past clicks are a predictor of future clicks on documents. However, when a new or unusual query appears, or when a system is not as widely used as a mainstream web search system, there may be little to no click data available to improve the results. Existing methods to boost query performance for sparse queries extend the query-document click relationship to more documents or queries, but require substantial clickthrough data from other queries. In this work we describe a way to boost rarely-clicked queries in a system where limited clickthrough data is available for all queries. We describe a probabilistic approach for carrying out that estimation and use it to rerank retrieved documents. We utilize information from co-click queries, subset queries, and synonym queries to estimate the clickthrough for a sparse query. Our experiments on a query log from a medical informatics company demonstrate that when overall clickthrough data is sparse, reranking search results using clickthrough information from related queries significantly outperforms reranking that employs clickthrough information from the query alone.
Starting Page	173
Ending Page	182
Page Count	10
File Format	PDF
ISBN	9781450307178
DOI	10.1145/2063576.2063606
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2011-10-24
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Query selection Sparse queries Sparse clickthrough data Reranking Query log mining
Content Type	Text
Resource Type	Article

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