PowerPoint

WIDM 2008Boosting the Ranking Function Learning Process using ClusteringOutlineIntroductionProblem definitionApproach EvaluationConclusion

IntroductionAbstractWeb continuously grows, the results returned by search engines are too many to reviewUser feedback has gained a lot of attentionRequire a big amount of user feedback on the results

Goal:Produce user feedback automatically by using some methods

Problem definitionUser feedbackExplicit feedback (user relevnace judgement)Implicit feedbackClick informationUsers usually inspect only the first few results returned by a search engine, and click even fewer Collect relevance judgements from clickthrough data is time consuming processProblem How to use explicit feedback to generate implicit feedback?(relevance relations expansion)

Approach procedureProcessAssume that only the relevance judgements of the top-10 results are available for each query (by BM25 feature)Group all the search results into clusters of documents having similar contentExpand the initial set(top-10 results) of relevance judgements using cluster informationClusteringRelation expansion

Train queryTrain queryexpansionRelation expansionExpansion Algorithm:

EvaluationDatasetLetor OHSUMED collection348,566 records and 16,140 relevance judgements84 training queries and 22 testing queriesRelevance judgement0(irrelevant), 1(partially relevant), 2(strongly relevant)Training methodRankSVM

EvaluationClustering precision

Evaluation

Use 160 relevance judgementsConclusionWe presented a methodology for increasing the training input of ranking function learning systemsFuture workDecision on whether a cluster is validDifferent Cluster label ways