Collaboratively augmented UIP – Filtered RIP with relevancy mapping for personalization of web search

Abstract

Personalized information retrieval has become more crucial and challenging in today’s time, as every user is demanding information according to their own perspectives. To achieve personalization, this paper proposes a methodology with a focus on the selection of an effective composition corresponding to various supporting modules of a personalization methodology. Collaborative tagging can be quite helpful in constructing User Interest Profile (UIP) and Resource Illustration Profile (RIP). The proposed methodology also focuses on UIP augmentation using multiple strategies; and a novel approach has also been designed to handle outlier tags which caused ambiguity in collective RIP. Even a good UIP and RIP alone cannot create an efficient personalization methodology; they also require a suitable mapping with user’s query requirement. Therefore, in the proposed methodology, the fuzzy satisfaction requirement-based novel mapping functions have been designed to measure query relevance score and user interest relevance score for a web resource. These scores have been further used to calculate the post-relevance score of a web resource after a suitable trade-off. Experiments using the del.icio.us dataset show that the proposed methodology has outperformed each and every baseline by a considerable margin.