Call For Paper Volume:4 Issue:10 Oct'2017 |

CATEGORIZING WEB SEARCH RESULTS FROM SEARCH ENGINE LOGS

Publication Date : 01/09/2015



Author(s) :

Darshana S. Parikh , Shankar M. Patil.


Volume/Issue :
Volume 2
,
Issue 8
(09 - 2015)



Abstract :

For a query, search engine provides platform for users. Different users have different search goals when they submit it to a search engine. The user search goals can be very useful in improving search engine relevance end-user experience. To increase retrieval precision, some new search engines provide manually verified answers to Frequently Asked Queries (FAQs). An underlying task is the identification of FAQs. This paper describes our attempt to cluster similar queries according to their contents as well as user logs. Our preliminary results show that the resulting clusters provide useful information for FAQ identification. Feedback sessions are constructed from user click-through logs and can efficiently reflect the information needs of users. The pseudo-documents are clustered using Fuzzy C Means, the fuzzy similarity based self- constructing algorithm. A novel optimization method is used to map feedback sessions to pseudo-documents which can efficiently reflect user information needs and finally, a new criterion “Classified Average Precision (CAP)” is used 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


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CATEGORIZING WEB SEARCH RESULTS FROM SEARCH ENGINE LOGS

August 31, 2015