Clu-PoF-A Novel Post Filtering Approach for Efficient Context Aware Recommendations
Anu Taneja, Anuja Arora · Procedia Computer Science · 2017
Recommendation Systems have gained huge importance in recent years as they proffer most admissible choices explored out of large data sets and are analogous to the user’s interests. But the notion of context – that describes the user, item or how users interact with the recommendation system has become one of the most significant factors that has increased the potency of recommendation systems in distinct domains such as e-commerce, movies, music, tourism etc. The main key issues in context aware recommendations are how to utilize the contextual information efficiently. So to utilize this information efficiently, an innovative cluster based context-aware post-filtering approach has been propounded that implicitly segregates the users into clusters on the basis of high performing contextual dimensions; combine the clusters on the basis of contextual similarity and then re-rank the items according to the probability of relevance in a cluster. The proposed approach is validated on movies dataset consisting of different contextual dimensions. The empirical analysis validates that the propounded approach surpass various state-of-the-art approaches.