Enriching query flow graphs with click information

M. Dyaa Albakour*, Udo Kruschwitz, Ibrahim Adeyanju, Dawei Song, Maria Fasli, Anne De Roeck

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

3 Citations (Scopus)

Abstract

The increased availability of large amounts of data about user search behaviour in search engines has triggered a lot of research in recent years. This includes developing machine learning methods to build knowledge structures that could be exploited for a number of tasks such as query recommendation. Query flow graphs are a successful example of these structures, they are generated from the sequence of queries typed in by a user in a search session. In this paper we propose to modify the query flow graph by incorporating clickthrough information from the search logs. Click information provides evidence of the success or failure of the search journey and therefore can be used to enrich the query flow graph to make it more accurate and useful for query recommendation. We propose a method of adjusting the weights on the edges of the query flow graph by incorporating the number of clicked documents after submitting a query. We explore a number of weighting functions for the graph edges using click information. Applying an automated evaluation framework to assess query recommendations allows us to perform automatic and reproducible evaluation experiments. We demonstrate how our modified query flow graph outperforms the standard query flow graph. The experiments are conducted on the search logs of an academic organisation's search engine and validated in a second experiment on the log files of another Web site.

Original languageEnglish
Title of host publicationInformation Retrieval Technology - 7th Asia Information Retrieval Societies Conference, AIRS 2011, Proceedings
Pages193-204
Number of pages12
DOIs
Publication statusPublished - 2011
Externally publishedYes
Event7th Asia Information Retrieval Societies Conference, AIRS 2011 - Dubai, United Arab Emirates
Duration: 18 Dec 201120 Dec 2011

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume7097 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference7th Asia Information Retrieval Societies Conference, AIRS 2011
Country/TerritoryUnited Arab Emirates
CityDubai
Period18/12/1120/12/11

Keywords

  • Automatic Evaluation
  • Query Suggestions
  • Search Log Analysis

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