A multi-attributed hybrid re-ranking technique for diversified recommendations

Recommender systems which are simulations of web personalization are now-a-days widely integrated in various domains for improving quality of fetched information. The most critical job for any recommender system is to provide more and more utilizable items to its users. Today lots of knowledge discovery techniques are invented to dig into huge chunks of user-item information and accurately read users mind for selecting list of items. Recent studies have shown the importance of diversified recommendations to increase item utilization. In short both accurate and diversified recommendations are key quality factors of user satisfaction. Re-ranking techniques are one of the solutions proven to be best for balancing both accuracy and diversity. After using collaborative filtering for standard ranking, we propose use of various content based attributes for re-ranking recommendations to introduce aggregate diversity selectively and flexibly. We experimented on movilens dataset and finally propose multi-attributed hybrid movie's content based re-ranking technique (MCBRT) for recommendation.