Query Recommendation Using Large-Scale Web Access Logs and Web Page Archive

Authors:
Lin Li;Shingo Otsuka;Masaru Kitsuregawa
Affiliations:
Dept. of Info. and Comm. Engineering, The University of Tokyo, Tokyo, Japan 153-8505;National Institute for Materials Science, Tsukuba, Japan 305-0047;Dept. of Info. and Comm. Engineering, The University of Tokyo, Tokyo, Japan 153-8505
Venue:
DEXA '08 Proceedings of the 19th international conference on Database and Expert Systems Applications
Year:
2008

Citing 9
Cited 3

Characterizing browsing strategies in the World-Wide Web

Proceedings of the Third International World-Wide Web conference on Technology, tools and applications
Query expansion using local and global document analysis

SIGIR '96 Proceedings of the 19th annual international ACM SIGIR conference on Research and development in information retrieval
Agglomerative clustering of a search engine query log

Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Community search assistant

Proceedings of the 6th international conference on Intelligent user interfaces
Query clustering using user logs

ACM Transactions on Information Systems (TOIS)
Creating a Web community chart for navigating related communities

Proceedings of the 12th ACM conference on Hypertext and Hypermedia
Query Expansion by Mining User Logs

IEEE Transactions on Knowledge and Data Engineering
Personalized query expansion for the web

SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Query expansion using web access log files

DEXA'05 Proceedings of the 16th international conference on Database and Expert Systems Applications

Query Recommendation for Improving Search Engine Results

International Journal of Information Retrieval Research
BidTerm Suggestion for Advertising Webpages

ASONAM '12 Proceedings of the 2012 International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2012)
An efficient approach to suggesting topically related web queries using hidden topic model

World Wide Web

Quantified Score

Hi-index	0.01

Visualization

Abstract

Query recommendation suggests related queries for search engine users when they are not satisfied with the results of an initial input query, thus assisting users in improving search quality. Conventional approaches to query recommendation have been focused on expanding a query by terms extracted from various information sources such as a thesaurus like WordNet, the top ranked documents and so on. In this paper, we argue that past queries stored in query logs can be a source of additional evidence to help future users. We present a query recommendation system based on large-scale Web access logs and Web page archive, and evaluate three query recommendation strategies based on different feature spaces (i.e., noun, URL, and Web community). The experimental results show that query logs are an effective source for query recommendation, and the Web community-based and noun-based strategies can extract more related search queries than the URL-based one.