A Hybrid Approach Towards Content Boosted Recommender System

With the exponential increase in data over the web the users face the problem in retrieving relevant knowledge. For eliminating this problem recommenders are used. They are based on one of the traditional recommendation approaches – content based approach and collaborative based approach. Recommendation can be provided to users using past user activities with help of data mining concepts and the market trend can be merged with it to provide optimized results from recommender. The user profile similarity for personalization, the hit based approach for new movies, history based approach all tackle one problem or the other faced by the traditional recommender systems. The paper proposes a new hybrid approach which combines the effect and positive functionality of all the above methods and tries to tackle major problems faced by recommender systems. The approach can be used to develop web based applications in other domains as well. The approach can be further refined by considering additional parameters based on the system’s need. DOI: 10.17762/ijritcc2321-8169.15050