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The large amount of information available on the internet initiated various Recommender algorithms to act as an intermediate between number of choices and internet users. Collaborative filtering is one of the most traditional and intensively used recommendation approaches for many commercial services. Despite providing satisfying outcomes, it does have some issues that include source diversity, reliability, sparsity of data, scalability and cold start. Thus, there is a need for further improvement in the current generation of recommender system to achieve a more effective human decision support in a wide variety of applications and scenarios. Personalized Expert based collaborative filtering (PReC) approach is proposed to identify domain specific experts and the use of experts preference enhanced the performance of collaborative filtering recommender systems. A unified framework is proposed that integrates similar users rating data, experts rating and demographic data to reduce the number of pairwise computations from the search space to ensure scalability and enabled fine grained recommendations. The proposed method is evaluated using accuracy metrics MAE, RMSE on the data set collected from MovieLens datasets.

Personalized Recommender by Exploiting Domain based Expert for Enhancing Collaborative Filtering Algorithm :PReC