A Recommendation Algorithm Using Hybrid Clustering

Recommendation algorithms apply knowledge discovery techniques to the problem of making product recommendation during a live interaction and achieving widespread success in E-commerce nowadays. With the magnitudes of users and items grow rapidly, resulting in the extreme sparsity of user rating data and the decreasing of real time performance. To solve the problem, the paper proposed a personalized recommendation algorithm using hybrid clustering technology. Users are clustered based on users' ratings on items. Then, the proposed approach utilizes the item clustering collaborative filtering to produce the recommendations. The recommendation joining user clustering and item clustering collaborative filtering is more scalable than the traditional one.