Hybrid personalized recommender system using centering-bunching based clustering algorithm

In recent years, there is overload of products information on world wide web. A personalized recommendation is an enabling mechanism to overcome information overload occurred when shopping in an Internet marketplace. This paper proposes a novel centering-bunching based clustering (CBBC) algorithm which is used for hybrid personalized recommender system (CBBCHPRS). The proposed system works in two phases. In the first phase, opinions from the users are collected in the form of user-item rating matrix. They are clustered offline using CBBC into predetermined number clusters and stored in a database for future recommendation. In the second phase, the recommendations are generated online for active user using similarity measures by choosing the clusters with good quality rating. This helps to get further effectiveness and quality of recommendations for the active users. The experimental results using Iris dataset show that the proposed CBBC performs better than K-means and new K-medodis algorithms. The performance of CBBCHPRS is evaluated using Jester database available on website of California University, Berkeley and compared with ants recommender system (ARS). The results obtained empirically demonstrate that the proposed CBBCHPRS performs superiorly and alleviates problems such as cold-start, first-rater and sparsity.