A NARRATIVE LEARNING TECHNIQUE IN SEARCH ENGINE USING FEEDBACK SESSIONS FOR USERS SEARCH GOALS
Abstract
The new learning process is for reducing the unwanted results in the search from the search-engine. This can be achieved through gathering feedback sessions. For this we propose a new approach to infer user search goals by analyzing search engine query logs. Initially we find multiple user search intensions; secondly we propose a novel framework to clustering the user feedback sessions. These feedbacks are gathered by click through logs. We use pseudo-documents for clustering these collected user feedbacks. Externally we use a new stranded Classified Common Accuracy (CCA) for evaluating the performance of inferring users search goals. This will saves time and cut down the process costs.
Keywords
References
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