Show simple item record

dc.contributor.authorYang, Haixuan
dc.identifier.citationMa, Hao and Yang, Haixuan and King, Irwin and Lyu, Michael R (2008) Learning latent semantic relations from clickthrough data for query suggestion Proceeding of the 17th ACM conference on Information and knowledge managementen_US
dc.descriptionJournal articleen_US
dc.description.abstractFor a given query raised by a specific user, the Query Suggestion technique aims to recommend relevant queries which potentially suit the information needs of that user. Due to the complexity of the Web structure and the ambiguity of users' inputs, most of the suggestion algorithms suffer from the problem of poor recommendation accuracy. In this paper, aiming at providing semantically relevant queries for users, we develop a novel, effective and efficient two-level query suggestion model by mining clickthrough data, in the form of two bipartite graphs (user-query and query-URL bipartite graphs) extracted from the clickthrough data. Based on this, we first propose a joint matrix factorization method which utilizes two bipartite graphs to learn the low-rank query latent feature space, and then build a query similarity graph based on the features. After that, we design an online ranking algorithm to propagate similarities on the query similarity graph, and finally recommend latent semantically relevant queries to users. Experimental analysis on the clickthrough data of a commercial search engine shows the effectiveness and the efficiency of our method.en_US
dc.relation.ispartofProceeding of the 17th ACM conference on Information and knowledge managementen
dc.subjectQuery suggestion techniqueen_US
dc.titleLearning latent semantic relations from clickthrough data for query suggestionen_US
dc.typeConference Paperen_US
dc.local.contactHaixuan Yang, School Of Mathematics,Statistics, & Applied Mathematics, Adb-G013, Nui Galway. 2320 Email:

Files in this item

Attribution-NonCommercial-NoDerivs 3.0 Ireland
This item is available under the Attribution-NonCommercial-NoDerivs 3.0 Ireland. No item may be reproduced for commercial purposes. Please refer to the publisher's URL where this is made available, or to notes contained in the item itself. Other terms may apply.

The following license files are associated with this item:


This item appears in the following Collection(s)

Show simple item record