Movie Recommendation on Web using Ontology and User Defined Tags

Internet provides great amount of heterogeneous information. Thousands of news articles and blogs post each day. Millions of movies, books, music tracks are becoming available on internet. But we really need and consume only few of them. To recommend to us something we may like, we need an intelligent Web Recommendation system. Recommendation Systems are limited by several problems, of which are sparsity, and the new user problem[1]. They also fail to make full use and harness the power of domain knowledge and semantic web ontology[1]. Use of Ontology with relations provides better interpretability of recommendation results. Diversity of standards, languages, protocols, and hardware components leads to important incompatibility issues when designing and developing multiplatform multimedia systems. Furthermore, user and community requirements and preferences should be taken into account when instantiating and configuring these kind of systems [11]. The goal of the proposed system is to develop a web recommendation system by using the concept of domain ontology and user provided tags. Introduction of user provided tags which acts as the labels or meta data are becoming very familiar and popular. In the proposed system tags and the concepts of domain ontology are processed using hybrid similarity to provide the results of recommendation. The system would benefit those users who have to scroll through pages of results to find relevant contents. The proposed measure based on hybrid similarity can be adopted effectively in this application of movie recommendation. General Terms Recommendation, weblog, sparsity. MS Word, web.