Enhance web search results using user feedback sessions
Journal Title: International Journal for Research in Applied Science and Engineering Technology (IJRASET) - Year 2015, Vol 3, Issue 4
Abstract
Most search engines in use today present the user a single ordered list of documents matching the search query. Since most users do not browse past the first page of search results, this method of displaying results often limits the effectiveness of the search to the relevance of the first several documents. An alternative to a single ordered list is to cluster the search results in that cluster are then displayed in a list. Under the assumption that documents which are similar to each other are likely to be relevant to the same query, the clustering of search results are easier to browse than a single ordered list. The inference and analysis of user search goals for a query can be very useful in improving search engine relevance and user experience. A novel approach is used to infer the user search goals by analyzing the user click through logs. So we first propose a framework to find out different user search goals for a query by clustering the proposed feedback sessions. The Feedback sessions are constructed from user click-through data which can efficiently reflect the information needs of users. Then a novel approach is used to generate the pseudo documents by considering the feedback sessions. The pseudodocuments are clustered using the suffix tree clustering algorithm to restructure the search results. In order to evaluate the restructured results, we use a method called “Classified Average Precision (CAP)” to measure the performance of inferring user search goals.
Authors and Affiliations
S. R. Sri Abirami, Dr. C. Nalini, A. P. Ponselvakumar
Keywords
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