Precision based recommender system using ontology

Recommender Systems (RS) have gradually progressed from the novelties used by few E-business sites to an important component of business tools dealing the world of E-commerce. RS are broadly used for product recommendations such as news and movies as well as, it is also gaining ground in service recommendations such as hotels, restaurants and travel attractions. Very few researchers have contributed for the recommendation of News using ontology. RS based on Clustering technique is developed having the initial step of converting research papers into a Feature Matrix (FM). This step is greatly refined by using semantic similarity between terms. In this paper Wordnet based Synset grouping approach is presented that not only reduces dimensions in FM but also generates Feature vectors (FV) based on keyword present in the news for each cluster with significantly improved cluster quality. The paper presents a grouping of news and recommendations of news according to user request using Feature vector. 20News dataset is used for experiment covering politics, electronics, computer and automobile domain.