Title

Collaborative Filtering Approach of Recommender System with Application in Amazon’s Jewelry Products

Presenter Information/ Coauthors Information

Md Riaz Ahmed Khan, South Dakota State University

Presentation Type

Event

Abstract

items, recommender system makes recommendation to the users based on their past behaviors. With increasing popularity, recommender system found its application in movies, music, videos, jokes, driving routes, restaurants and all kinds of general products. One common approach to build a recommender system is Collaborative Filtering (CF). In this work, we walk through the different steps of making recommender system based on different CB techniques (user based, item based, hybrid). We used Amazon’s review data of jewelry products to build different recommender systems and evaluate their performances.

Start Date

12-2-2018 12:00 PM

This document is currently not available here.

Share

COinS
 
Feb 12th, 12:00 PM

Collaborative Filtering Approach of Recommender System with Application in Amazon’s Jewelry Products

items, recommender system makes recommendation to the users based on their past behaviors. With increasing popularity, recommender system found its application in movies, music, videos, jokes, driving routes, restaurants and all kinds of general products. One common approach to build a recommender system is Collaborative Filtering (CF). In this work, we walk through the different steps of making recommender system based on different CB techniques (user based, item based, hybrid). We used Amazon’s review data of jewelry products to build different recommender systems and evaluate their performances.