Refinement of recommendations based on user preferences

Collaborative Filtering is one of the most researched techniques. It generates recommendations from similar taste users in a group. In this paper, Information Theoretic Techniques are used to propose an Online Recommendation Generator based on Collaborative Filtering. It initially generates preliminary recommendations based on positive and negative user preferences and further refines these preliminary recommendations based on opposite user preferences. Experiments are conducted using MovieLens Dataset and considerable improvement in accuracy is seen in the results.