Automatic Generation of Natural Language Explanations

TL;DR

This paper introduces a character-level RNN model that automatically generates natural language reviews for items, mimicking user-written explanations based on ratings, trained on real-world data, and achieving high-quality text generation.

Contribution

It presents a novel neural network approach for review generation that captures review style and content, advancing explainable recommendation systems.

Findings

Generated reviews are close to real user reviews in quality

The model captures negation, misspellings, and domain-specific vocabulary

Empirical results show high-quality natural language explanations

Abstract

An important task for recommender system is to generate explanations according to a user's preferences. Most of the current methods for explainable recommendations use structured sentences to provide descriptions along with the recommendations they produce. However, those methods have neglected the review-oriented way of writing a text, even though it is known that these reviews have a strong influence over user's decision. In this paper, we propose a method for the automatic generation of natural language explanations, for predicting how a user would write about an item, based on user ratings from different items' features. We design a character-level recurrent neural network (RNN) model, which generates an item's review explanations using long-short term memories (LSTM). The model generates text reviews given a combination of the review and ratings score that express opinions about different factors or aspects of an item. Our network is trained on a sub-sample from the large real-world dataset BeerAdvocate. Our empirical evaluation using natural language processing metrics shows the generated text's quality is close to a real user written review, identifying negation, misspellings, and domain specific vocabulary.