Probabilistic neural network approach to alleviate sparsity and cold start problems in collaborative recommender systems

Abstract

Collaborative Recommender system helps the online users to identify the right product during electronic purchasing. The collaborative recommender system identifies the similar users based on the purchasing or rating behavior to the active user and then recommends the product based on the similar users. Collaborative recommender system is widely used in majority of the existing online recommender system such as orkut, google, amazon, walmart etc. Besides it popularity, is suffers due to sparsity, cold start and scalability recommender system. Extensive research is going on to overcome these problems. In this paper, Probabilistic neural network (PNN) is used to calculate the trust between users based on rating matrix. Using the calculated trust, sparse rating matrix is smoothened, by predicting the rating values of the nonrated items in the rating matrix. Using this smoothened rating matrix, the trust is calculated for online active users. The calculated trust is used to recommend product. Experiments are conducted using dataset such as movielens. Based on the performance metrics, it is proved that the proposed method performs better than the benchmark and some existing systems.