An agglomerative hierarchical clustering for Hybrid Recommender Systems

In today´s world there is a tremendous increase in the number of users using the Internet. As a result the e-commerce websites have been emerging to encourage the users of Internet. But these e-commerce websites are facing the problem in recommending the items to the users. In view of this challenge, an Agglomerative Hierarchical Clustering for Hybrid Recommender Systems approach is proposed in this paper which aims at recruiting similar items in the same clusters to recommend items which are similar both at content as well as at rating. Technically, this approach can be performed in two stages. In the first stage, the available items are divided into small-scale clusters, for further processing. And in the second stage, Hybrid Recommender Systems algorithm is imposed on one of the clusters. As the number of the items in a cluster is much less than the total number of the item available on the web, it is expected that the online execution time of Hybrid Recommender Systems can be reduced. At last, to verify the availability of this approach several experiments are conducted.