Research Article
A New Algorithm for Inferring User Search Goals with Feedback Sessions
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
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| Volume 118 - Issue 14 |
| Published: May 2015 |
| Authors: Bhushan Thakare, Rohan Rawlani, Sahil Pathak, Dipali Salve, Ritesh Natekar |
10.5120/20812-3099
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Bhushan Thakare, Rohan Rawlani, Sahil Pathak, Dipali Salve, Ritesh Natekar . A New Algorithm for Inferring User Search Goals with Feedback Sessions. International Journal of Computer Applications. 118, 14 (May 2015), 9-12. DOI=10.5120/20812-3099
@article{ 10.5120/20812-3099,
author = { Bhushan Thakare,Rohan Rawlani,Sahil Pathak,Dipali Salve,Ritesh Natekar },
title = { A New Algorithm for Inferring User Search Goals with Feedback Sessions },
journal = { International Journal of Computer Applications },
year = { 2015 },
volume = { 118 },
number = { 14 },
pages = { 9-12 },
doi = { 10.5120/20812-3099 },
publisher = { Foundation of Computer Science (FCS), NY, USA }
}
%0 Journal Article
%D 2015
%A Bhushan Thakare
%A Rohan Rawlani
%A Sahil Pathak
%A Dipali Salve
%A Ritesh Natekar
%T A New Algorithm for Inferring User Search Goals with Feedback Sessions%T
%J International Journal of Computer Applications
%V 118
%N 14
%P 9-12
%R 10.5120/20812-3099
%I Foundation of Computer Science (FCS), NY, USA
Abstract
For a broad-topic and ambiguous query, different users may have different search goals when they submit it to a search engine. The inference and analysis of user search goals can be very useful in improving search engine relevance and user experience. In this paper, we propose a novel approach to infer user search goals by analyzing search engine query logs. First, we propose a framework to discover different user search goals for a query by clustering the proposed feedback sessions. Feedback sessions are constructed from user click-through logs and can efficiently reflect the information needs of users. Second, we propose a novel approach to generate pseudo-documents to better represent the feedback sessions for clustering. Finally, we propose a new criterion "Classified Average Precision (CAP)" to evaluate the performance of inferring user search goals. Experimental results are presented using user click-through logs from a commercial search engine to validate the effectiveness of our proposed methods.
References
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