Collaborative Filtering Approach of Recommender System with Application in Amazon’s Jewelry Products
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
2-12-2018 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.