Fuzzy C Means Algorithm for inferring User Search Goals with Feedback Sessions
N. Vidhyapriya, S. Sampath · IJARCCE · 2015
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.The project proposes a novel approach to infer user search goals by analyzing search engine query logs.First, it proposes a framework to discover different user search goals for a query by clustering the proposed feedback sessions.The feedback session is defined as the series of both clicked and unclicked URLs and ends with the last URL that was clicked in a session from user click-through logs.Second, the pseudo-documents are produced to better represent the feedback sessions for clustering.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.