Learning from past queries for resource selection

Authors:
Suleyman Cetintas;Luo Si;Hao Yuan
Affiliations:
Purdue University, West Lafayette, IN, USA;Purdue University, West Lafayette, IN, USA;Purdue University, West Lafayette, IN, USA
Venue:
Proceedings of the 18th ACM conference on Information and knowledge management
Year:
2009

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Abstract

Federated text search provides a unified search interface for multiple search engines of distributed text information sources. Resource selection is an important component for federated text search, which selects a small number of information sources that contain the largest number of relevant documents for a user query. Most prior research of resource selection focused on selecting information sources by analyzing static information of available information sources that is sampled in the offline manner. On the other hand, most prior research ignored a large amount of valuable information like the results from past queries. This paper proposes a new resource selection technique (which is called qSim) that utilizes the search results of past queries for estimating the utilities of available information sources for a specific user query. Experiment results demonstrate the effectiveness of the new resource selection algorithm.