Extended Recommendation Framework: Generating the Text of a User Review as a Personalized Summary

TL;DR

This paper introduces a system that enhances recommender systems by generating personalized user reviews, combining ratings, item information, and polarity to provide comprehensive, tailored recommendations.

Contribution

It presents a novel extractive review generation method that integrates ratings and item data, along with a personalized polarity classifier, to improve recommendation explanations.

Findings

Enhanced review generation performance using combined data sources

Improved recommendation explanations with personalized polarity insights

System provides three personalized recommendation hints: rating, text, and polarity

Abstract

We propose to augment rating based recommender systems by providing the user with additional information which might help him in his choice or in the understanding of the recommendation. We consider here as a new task, the generation of personalized reviews associated to items. We use an extractive summary formulation for generating these reviews. We also show that the two information sources, ratings and items could be used both for estimating ratings and for generating summaries, leading to improved performance for each system compared to the use of a single source. Besides these two contributions, we show how a personalized polarity classifier can integrate the rating and textual aspects. Overall, the proposed system offers the user three personalized hints for a recommendation: rating, text and polarity. We evaluate these three components on two datasets using appropriate measures for each task.