Review Based Rating Prediction

TL;DR

This paper presents a review-based recommendation system that leverages natural language processing of user reviews to improve rating predictions and provide more relevant personalized recommendations.

Contribution

It introduces a novel review mining approach that uses textual review analysis to infer user context and enhance rating prediction accuracy.

Findings

System improves rating prediction accuracy over standard methods

Utilizes NLP techniques to analyze textual reviews for context inference

Produces more relevant personalized recommendations

Abstract

Recommendation systems are an important units in today's e-commerce applications, such as targeted advertising, personalized marketing and information retrieval. In recent years, the importance of contextual information has motivated generation of personalized recommendations according to the available contextual information of users. Compared to the traditional systems which mainly utilize users' rating history, review-based recommendation hopefully provide more relevant results to users. We introduce a review-based recommendation approach that obtains contextual information by mining user reviews. The proposed approach relate to features obtained by analyzing textual reviews using methods developed in Natural Language Processing (NLP) and information retrieval discipline to compute a utility function over a given item. An item utility is a measure that shows how much it is preferred according to user's current context. In our system, the context inference is modeled as similarity between the users reviews history and the item reviews history. As an example application, we used our method to mine contextual data from customers' reviews of movies and use it to produce review-based rating prediction. The predicted ratings can generate recommendations that are item-based and should appear at the recommended items list in the product page. Our evaluations suggest that our system can help produce better prediction rating scores in comparison to the standard prediction methods.