Utilizing Textual Reviews in Latent Factor Models for Recommender Systems

TL;DR

This paper introduces a recommender system that integrates textual reviews and additional user/item features with latent factor models, demonstrating improved accuracy on large datasets.

Contribution

It combines latent factor and topic models with extra features, extending existing methods to handle large-scale datasets effectively.

Findings

Combining reviews with ratings improves recommendation quality.

Adding user and item features enhances prediction accuracy.

The approach performs well on large Amazon datasets.

Abstract

Most of the existing recommender systems are based only on the rating data, and they ignore other sources of information that might increase the quality of recommendations, such as textual reviews, or user and item characteristics. Moreover, the majority of those systems are applicable only on small datasets (with thousands of observations) and are unable to handle large datasets (with millions of observations). We propose a recommender algorithm that combines a rating modelling technique (i.e., Latent Factor Model) with a topic modelling method based on textual reviews (i.e., Latent Dirichlet Allocation), and we extend the algorithm such that it allows adding extra user- and item-specific information to the system. We evaluate the performance of the algorithm using Amazon.com datasets with different sizes, corresponding to 23 product categories. After comparing the built model to four other models we found that combining textual reviews with ratings leads to better recommendations. Moreover, we found that adding extra user and item features to the model increases its prediction accuracy, which is especially true for medium and large datasets.