An Efficient Recommender System using Hierarchical Clustering Algorithm
Prabhat Kumar, Sherry Chalotra · 2014
The massive growth of information these days has created the need for information filtering techniques that help users filter out extraneous content to identify the right information they need to make important decisions. The right information they need to make important decisions. Recommender systems are one approach to this problem, based on presenting potential items of interest to a user rather than requiring the user to go looking for them. Recommender system is a subclass of information retrieval system and information filtering system that seek to predict the 'rating' or 'preference' that user would give to an item. The concept of recommender system grows out of the idea of the information reuse and persistent preferences. Recommender systems have recently gained much attention as a new business intelligence tool for e-commerce business. Applying a recommender system for an online retailer store helps to enhance the quality of service for customers and increase the sale of products and services. In order to recommend items for particular requests the system has to perform large searching, sorting, and filtering and huge matrix operations. This will be a very time consuming operation even for smaller searching operations. Therefore there will be a need of an efficient framework to predict or recommend an item within time bounds. Almost all the recommenders proposed earlier uses continuous algorithms but the nature of the items is discrete and for computer systems the performance of discrete algorithms is much better as compared to continuous algorithms. In this paper a User-User based Collaborative Recommender system has been proposed that makes use of discrete cluster algorithms to enhance the recommendations and improve running time.