Amazon Product Recommender System

TL;DR

This paper develops and evaluates a deep neural network model to predict customer review ratings for digital music tracks on Amazon, using a large dataset of reviews to improve recommendation accuracy.

Contribution

It introduces a novel deep neural network architecture for predicting review ratings, outperforming traditional models on Amazon music review data.

Findings

DNN achieved higher prediction accuracy than traditional models.

The model effectively captures review rating patterns for digital music.

Results demonstrate the potential for improved recommender systems using deep learning.

Abstract

The number of reviews on Amazon has grown significantly over the years. Customers who made purchases on Amazon provide reviews by rating the product from 1 to 5 stars and sharing a text summary of their experience and opinion of the product. The ratings of a product are averaged to provide an overall product rating. We analyzed what ratings score customers give to a specific product (a music track) in order to build a recommender model for digital music tracks on Amazon. We test various traditional models along with our proposed deep neural network (DNN) architecture to predict the reviews rating score. The Amazon review dataset contains 200,000 data samples; we train the models on 70% of the dataset and test the performance of the models on the remaining 30% of the dataset.