WSEAS Transactions on Computers
Print ISSN: 1109-2750, E-ISSN: 2224-2872
Volume 13, 2014
Improvising Web Search Using Concept Based Clustering
Authors: , ,
Abstract: The user profile is an elementary component of any application based on personalization. The existing strategies of user profile considers only objects which interests the user (positive preferences of the user), and not on objects which does not interest the user (negative preferences of the user). This paper focuses on personalization in search engine and the proposed approach consists of three steps. At first, an algorithm for concept extraction is employed in which concepts are extracted and the relations between these concepts are obtained from the web-snippets returned by the search engine. Second, a user profile strategy is employed to build a concept-based user profile which predicts the conceptual preferences of the user. Building user profile comprises of identifying the concept preference pair by Spy Naive Bayes Classifier (Spy NB-C) method and learning the users preferences represented by feature weights vectors by Ranking-Support Vector Machine(RSVM). Third, the concept relations together with the predicted conceptual preferences of the user, is given as input to personalized concept-based clustering algorithm to find the conceptually related queries. To cluster ambiguous queries into different clusters of queries a personalized clustered query-concept bi-partite graph is created by making use of the extracted concepts and click through data. This suggested personalized query recommendations to the individual users based on their interests. From the experimental results, it is observed that the user profile which captured both the preferences of the user increased the separation between dissimilar queries and similar queries. Improvements in F-measure and DCG score shows that the quality of query clusters resulted provided personalised results to the users.
Search Articles
Keywords: concept extraction, negative preferences, personalization, concept clusters, search engine, user profile
Pages: 338-350
WSEAS Transactions on Computers, ISSN / E-ISSN: 1109-2750 / 2224-2872, Volume 13, 2014, Art. #29