Automatic Generation of Natural Language Explanations
This addresses the need for more natural, review-like explanations in recommender systems to better influence user decisions, though it is incremental as it builds on existing neural methods for text generation.
The paper tackles the problem of generating natural language explanations for recommender systems by predicting how a user would write reviews based on item features and ratings, using a character-level RNN with LSTM, and shows that the generated text quality is close to real user reviews in terms of identifying negation, misspellings, and domain-specific vocabulary.
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.