Open AccessJournal Article
User search feedback behavior using fuzzy c mean
Darshana S. Parikh,Navi Mumbai +1 more
TLDR
This paper is clustering the feedback session by using Fuzzy c-means algorithm and uses this method to map feedback sessions to pseudo-documents which can efficiently reflect required data.Abstract:
When a query is submitted to search engine, user have in mind a fixed goal. Search engine gives thousands of results for such a query. Most of them are not useful for user so time and energy is wasted. For increasing retrieval precision, some new method provides manually verified answers to Frequently Asked Queries (FAQs). In this paper we are clustering the feedback session by using Fuzzy c-means algorithm. Also we use method to map feedback sessions to pseudo-documents which can efficiently reflect required data. Then, we evaluate the "Classified Average Precision (CAP)" of restructured web search results. In the area of web mining, more importance is given to fast and accurate extraction of information. Query suggestions provided by the search engine will help to find the user needs. But it may cover broad topics, so this may not be solution for achieving a better search result. Also same queries have different goals for different users. The analysis of user search goal improves the relevance and user satisfaction of the search engine. This method analyzes the user query and restructure the search results.read more
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Proceedings ArticleDOI
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TL;DR: This work presents a real-world study of modeling the behavior of web search users to predict web search result preferences and generalizes the approach to model user behavior beyond clickthrough, which results in higher preference prediction accuracy than models based on clickthrough information alone.
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TL;DR: The attempt to cluster similar queries according to their contents as well as user logs is described, and preliminary results show that the resulting clusters provide useful information for FAQ identification.
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TL;DR: This paper presents the results from a human subject study that strongly indicate the feasibility of automatic query-goal identification, and proposes two types of features for the goal-identification task: user-click behavior and anchor-link distribution.