User Profiling for Recommendation System

TL;DR

This paper discusses user profiling techniques for recommendation systems, emphasizing dataset analysis with Weka and proposing a novel approach to improve personalization and item ranking accuracy.

Contribution

It introduces a new user profiling method tailored for recommendation systems, utilizing dataset analysis and Weka tools for enhanced personalization.

Findings

Identified interesting dataset features using Weka.

Proposed a novel user profiling technique.

Improved recommendation relevance and accuracy.

Abstract

Recommendation system is a type of information filtering systems that recommend various objects from a vast variety and quantity of items which are of the user interest. This results in guiding an individual in personalized way to interesting or useful objects in a large space of possible options. Such systems also help many businesses to achieve more profits to sustain in their filed against their rivals. But looking at the amount of information which a business holds it becomes difficult to identify the items of user interest. Therefore personalization or user profiling is one of the challenging tasks that give access to user relevant information which can be used in solving the difficult task of classification and ranking items according to an individuals interest. Profiling can be done in various ways such assupervised or unsupervised, individual or group profiling, distributive or and non distributive profiling. Our focus in this paper will be on the dataset which we will use, we identify some interesting facts by using Weka Tool that can be used for recommending the items from dataset. Our aim is to present a novel technique to achieve user profiling in recommendation system.