Information

Abstract

The project "Personalized Recommend System for Supeshop" intends to develop a shop system for the next generation Supeshop Recommend System platform. The project is driven by research where customers can find product recommended in category wise. Recommendation system is one of the stronger tools to increase profit and retaining buyer. This project proposes a quick and intuitive product recommendation system that helps customer to find appropriate product to buy. We used a collaborative filtering method based on correlation coefficient. In the future business intelligence will be added for further enhancements.

Objectives

The main objective of this project is to build an optimized user friendly , which will be breakdown the traditional system and create a product recommendation system.

This system saves the precious time of user's/customer .
Provides large number of choices for product & also recommend for product.
The system recommending algorithm scale well with co-rated product items.
It’s very efficient to use.
To analyze the users data for recommending new product.

Experimental Result

In our research-based project, we collated data set and pre-process that. Add also the new attribute. We apply three types of algorithm.That’s name is Decision tree, Association Rule and ZeroR algorithm. when we are using the ZeroR we find out better result than other algorithms. So, we accepted the result for our research-based project.

Research Goal

Human evaluations for product.
Large number of choices for product.
Saving time for recommended product.
User finds clear information about product in recommendation system.

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