TransRev: Modeling Reviews as Translations from Users to Items

TL;DR

TransRev is a novel recommendation approach that models reviews as translations between user and item embeddings, integrating sentiment analysis and multi-relational learning to improve prediction accuracy and interpretability.

Contribution

It introduces a joint learning framework that embeds users, items, and reviews, enabling review translation modeling for enhanced recommendation and review retrieval.

Findings

Outperforms state-of-the-art recommender systems on benchmark datasets.

Effectively retrieves similar review texts based on embedding similarity.

Integrates sentiment analysis with recommendation modeling.

Abstract

The text of a review expresses the sentiment a customer has towards a particular product. This is exploited in sentiment analysis where machine learning models are used to predict the review score from the text of the review. Furthermore, the products costumers have purchased in the past are indicative of the products they will purchase in the future. This is what recommender systems exploit by learning models from purchase information to predict the items a customer might be interested in. We propose TransRev, an approach to the product recommendation problem that integrates ideas from recommender systems, sentiment analysis, and multi-relational learning into a joint learning objective. TransRev learns vector representations for users, items, and reviews. The embedding of a review is learned such that (a) it performs well as input feature of a regression model for sentiment prediction; and (b) it always translates the reviewer embedding to the embedding of the reviewed items. This allows TransRev to approximate a review embedding at test time as the difference of the embedding of each item and the user embedding. The approximated review embedding is then used with the regression model to predict the review score for each item. TransRev outperforms state of the art recommender systems on a large number of benchmark data sets. Moreover, it is able to retrieve, for each user and item, the review text from the training set whose embedding is most similar to the approximated review embedding.