抽象的

Enhancing the Search Result for User Query Using Iterative User Feedback

PriyaSurana, PrashantJamdade, TejasNandedkar, Akshay S. Ghadge, NirbhayBobde

In real scenario when users submit a search query to a search engine, each user may have different search goals. We can improve search engine relevance by analyzing user search goals. We present an approach that infers user search goals by analyzing search engine query logs. Our approach discovers different user search goals for a query by clustering the proposed feedback sessions. Users information needs can be captured with the help of feedback sessions. Feedback sessions are constructed from user click-through logs and can efficiently reflect the information needs of users on results. Secondly, we also generate pseudo-documents for better representation of the feedback sessions for clustering. Finally, we present Classified Average Precision (CAP) to evaluate the performance of inferring user search goals. For an 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 present a novel approach to Enhance Search Result For User Query Using Iterative User Feedback.