A comprehensive study of recommendation system on different domains

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Rabinarayan Satpathy

Trident Group of Institutions–Odisha, India

: J Comput Eng Inf Technol

Abstract

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The way in which the growth in the available digital information and the number of users on the internet has created a challenge of data overload, causes timely access to the item of interest on the internet. Many of the information retrieval systems try to solve this problem, but prioritization of item and user were absent in most of the systems. The purpose of this paper is to develop a recommender system using item-based collaborative filtering technique and K-means. Out of all techniques, the collaborative filtering technique is the most popular algorithm in the recommender system’s field. Recommender systems are the information filtering systems that deal with the problem of information overload by filtering vital information fragment out of a large amount of dynamically generated information according to user’s interest, preferences or observed behavior about them. This paper considers m users, n number of items and presents an approach based on K-means clustering to produce a recommendation for the active user by a new approach. Here K-means clustering algorithm is used to categorize users based on their interests.

Biography

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