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.

1. G Nizar R. Mabroukeh, C. I. Ezeife , "Ontology based Web Recommendation from tags", IEEE 2011 27th International conference on data engineering.
2. Mark Melenhorst Mark van Setten," Usefulness of Tags in providing access to large information system", IEEE 2007 International conference on hypermedia and multimedia.
3. Ana Belen Baragann Martinez, "What's on TV tonight? Effective and efficient personalized recommendation system on TV programs",IEEE 2009 transaction on consumer electronics.
4. A practical introduction to ontology and owl", Online material published by University of Manchester.
5. Song Hui, Lu Penduyu,Zhau Kai,"Improving item based collaborative filtering recommendation system with tag", IEEE 2011 International conference of world wide web.
6. Similarity measure based on edge counting", world academy of science, engineering and technology in 2006.
7. Antonia Dattolo, Felice Ferrara, Carlo Tasso, "Role of Tags for recommendation a survey", IEEE 2010 3rd International conference on human system interaction.
8. Freddy Lecue, " Combining collaborative filtering and semantic content based approaches to common web services", IEEE 2010 4th International conference on semantic web computing.
9. Kaushal Giri,"Role of Ontology in semantic web",DESIDOC Journal of Library and Information Technology , Vol. 31, No. 2, March 2011, pp 116-120.
10. Gruber, T. A translation approach to portable ontology specifications. Knowledge Acquisition, 1998, 5, 199.
11. Errnesto,Expositon,Jorgge,Montalvoo,Myriamm,Laamoa,"Mulimedia ontology driven architecture for Multimedia System", 2010 second International Conference on Information, Process and Knowledge management.

Ontology Tag Sparql Wordnet Rdf Owl. lcs