PERSONALISED RECOMMENDER ENGINE USINGA PROBABLISTIC MODEL

The world of e-commerce and e-business has opened many horizons to explore customers on web. Consumers are expecting businesses to approach and please them with their expectations. This has given rise to recommender systems. Many of the recommender systems are generalized in nature which are often based on market stratum and user predictions. However, extensive research is being carried out in providing personalized recommendations using association rules, customer segmentation, social media ontologies and demographics. There are many issues in the implementation of these systems. This paper discusses diverse recommender approaches proposed in the past with a comparative study and gap analysis. It also proposes a Hybrid Personalized Recommender system by using a probabilistic model.