A Social-knowledge-directed Query Suggestion Approach for Exploratory Search

Yuqing Mao, Haifeng Shen, Chengzheng Sun

    Research output: Contribution to conferencePaper

    2 Citations (Scopus)

    Abstract

    Existing query suggestion techniques mainly revolve around mining existing queries that are most similar to a given query. If the query fails to precisely capture a user's real intent, for example, in most exploratory search tasks, suggested queries are likely to fail as well. If suggested queries are not only relevant to the query but also diverse in nature, it is likely that some of them are close to the user's real intent. In this paper, we propose a novel social-knowledge-directed query suggestion approach for exploratory search, which integrates the social knowledge into the probabilistic model based on query-URL bipartite graphs. Social knowledge is discovered by conducting kernel principle component analysis on the related queries, and incorporating the social knowledge with random walk on the bipartite graph can obtain diverse queries that are relevant to a given one. We have conducted a set of experiments to validate this approach and the results show that this approach outperforms other query suggestion methods in terms of supporting exploratory search.

    Original languageEnglish
    Pages1-8
    Number of pages8
    DOIs
    Publication statusPublished - 19 Dec 2011
    EventInternational Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery -
    Duration: 10 Oct 2011 → …

    Conference

    ConferenceInternational Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery
    Period10/10/11 → …

    Keywords

    • diversity
    • exploratory search
    • knowledge discovery and sharing
    • query log analysis
    • query suggestion
    • social knowledge

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  • Cite this

    Mao, Y., Shen, H., & Sun, C. (2011). A Social-knowledge-directed Query Suggestion Approach for Exploratory Search. 1-8. Paper presented at International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery, . https://doi.org/10.1109/CyberC.2011.11