Recommender system using item based collaborative filtering (CF) and K-means

Abstract

The heightening in the available information in the form of digital data and the number of users on the Internet have engendered a challenge of overburden of data which obstructs access to interested item on the Internet timely. There are many information retrieval systems which try to solve the problem of information overloading but in their cases prioritization and personalization of information were absent. The main aim is to develop a recommender system using item based collaborative filtering technique and K-means. The most popular algorithm in the recommender system’s field is the collaborative filtering technique. Recommender systems are the filtering systems for information that concerned with the problem of information overburden by filtering essential information fragment out of enormous dynamically promoted information according to person’s attentiveness, taste and distinguished behavior about them. We are considering $m$ users, $n$ items (in numbers) and presenting a model to fabricate a recommendation for the mobile user by a new approach.