Hybrid Personalized Recommender System Using Fast K-medoids Clustering Algorithm

Recommender systems attempt to predict items in which a user might be interested, given some information about the user’s and items’ profiles. This paper proposes a fast k-medoids clustering algorithm which is used for Hybrid Personalized Recommender System (FKMHPRS). 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 fast k-medoids into predetermined number clusters and stored in a database for future recommendation. In the second phase, clusters are used as the neighborhoods, the prediction rating for the active users on items are computed by either weighted sum or simple weighted average. This helps to get more effective and quality recommendations for the active users. The experimental results using Iris dataset show that the proposed fast k-medoids performs better than k-medoids and k-mean algorithms. The performance of FKMHPRS is evaluated using Jester database available on website of California University, Berkeley and compared with web personalized recommender system (WPRS). The results obtained empirically demonstrate that the proposed FKMHPRS performs superiorly.