File Download
  Links for fulltext
     (May Require Subscription)
Supplementary

postgraduate thesis: A probabilistic approach to diversified query recommendation

TitleA probabilistic approach to diversified query recommendation
Authors
Advisors
Advisor(s):Kao, CM
Issue Date2012
PublisherThe University of Hong Kong (Pokfulam, Hong Kong)
Citation
Li, R. [李锐瑞]. (2012). A probabilistic approach to diversified query recommendation. (Thesis). University of Hong Kong, Pokfulam, Hong Kong SAR. Retrieved from http://dx.doi.org/10.5353/th_b4979975
AbstractThe effectiveness of keyword-based search engines depends largely on the ability of a user to formulate proper queries that are both expressive and selective. However, web search queries issued by casual users are often short and with limited expressiveness. Query recommendation is a popular technique employed by search engines to help users refine their queries. Traditional similarity-based methods, however, often result in redundant and monotonic recommendations. We identify five basic requirements of a query recommendation system, namely relevancy, redundancy-free, diversity, ranking and efficiency. In particular, we focus on the requirements of redundancy-free and diversified recommendations. We propose the DQR framework, which mines a search log to achieve two goals: (1) It clusters search log queries to extract query concepts, based on which recommended queries are selected. Through query construction from the query concepts, we are able to avoid recommendation redundancy. (2) It employs a probabilistic model and a greedy heuristic algorithm to achieve recommendation diversification. Through a comprehensive user study we compare DQR against five other recommendation methods on real search log datasets. Our experiment shows that DQR outperforms the other methods in terms of relevancy, diversity, and ranking performance of the recommendations. At the same time, DQR also achieves high efficiency performance.
DegreeMaster of Philosophy
SubjectQuerying (Computer science)
Probabilities.
Dept/ProgramComputer Science
Persistent Identifierhttp://hdl.handle.net/10722/181541

 

DC FieldValueLanguage
dc.contributor.advisorKao, CM-
dc.contributor.authorLi, Ruirui.-
dc.contributor.author李锐瑞.-
dc.date.accessioned2013-03-03T03:21:12Z-
dc.date.available2013-03-03T03:21:12Z-
dc.date.issued2012-
dc.identifier.citationLi, R. [李锐瑞]. (2012). A probabilistic approach to diversified query recommendation. (Thesis). University of Hong Kong, Pokfulam, Hong Kong SAR. Retrieved from http://dx.doi.org/10.5353/th_b4979975-
dc.identifier.urihttp://hdl.handle.net/10722/181541-
dc.description.abstractThe effectiveness of keyword-based search engines depends largely on the ability of a user to formulate proper queries that are both expressive and selective. However, web search queries issued by casual users are often short and with limited expressiveness. Query recommendation is a popular technique employed by search engines to help users refine their queries. Traditional similarity-based methods, however, often result in redundant and monotonic recommendations. We identify five basic requirements of a query recommendation system, namely relevancy, redundancy-free, diversity, ranking and efficiency. In particular, we focus on the requirements of redundancy-free and diversified recommendations. We propose the DQR framework, which mines a search log to achieve two goals: (1) It clusters search log queries to extract query concepts, based on which recommended queries are selected. Through query construction from the query concepts, we are able to avoid recommendation redundancy. (2) It employs a probabilistic model and a greedy heuristic algorithm to achieve recommendation diversification. Through a comprehensive user study we compare DQR against five other recommendation methods on real search log datasets. Our experiment shows that DQR outperforms the other methods in terms of relevancy, diversity, and ranking performance of the recommendations. At the same time, DQR also achieves high efficiency performance.-
dc.languageeng-
dc.publisherThe University of Hong Kong (Pokfulam, Hong Kong)-
dc.relation.ispartofHKU Theses Online (HKUTO)-
dc.rightsThe author retains all proprietary rights, (such as patent rights) and the right to use in future works.-
dc.rightsCreative Commons: Attribution 3.0 Hong Kong License-
dc.source.urihttp://hub.hku.hk/bib/B49799757-
dc.subject.lcshQuerying (Computer science)-
dc.subject.lcshProbabilities.-
dc.titleA probabilistic approach to diversified query recommendation-
dc.typePG_Thesis-
dc.identifier.hkulb4979975-
dc.description.thesisnameMaster of Philosophy-
dc.description.thesislevelMaster-
dc.description.thesisdisciplineComputer Science-
dc.description.naturepublished_or_final_version-
dc.identifier.doi10.5353/th_b4979975-
dc.date.hkucongregation2013-

Export via OAI-PMH Interface in XML Formats


OR


Export to Other Non-XML Formats