Fuzzy ontology based recommender system with diversification mechanism

Abstract

Recommender system is defined as technique that endeavors to recommend item by predicting users interest. Existing recommender system has various flaws like cold start problem, data sparsity, over specialization issues and scalability issues. Ontological information about a certain domain can be embedded with the recommender system so as to represent both user profiles and domain objects in more sophisticated and accurate way. It also handles better matching procedures with the help of semantic similarity measures. A fuzzy ontology could be employed as a means to address imprecise and vague information associated with user profile and domain. The advancement at the knowledge representation level and at the reasoning level lead to more accurate recommendations and to elevate the execution of recommender systems. Recommender system with diversification process may produce serendipitous results, which allow users to explore unexpected and relevant items. Diversification mechanism focused on pre clustered instance set of similar user, not only reduces the computational cost considerably but also gives the collaborative touch to the system.