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The text of a review expresses the sentiment a customer has towards aparticular product. This is exploited in sentiment analysis where machinelearning models are used to predict the review score from the text of thereview. Furthermore, the products costumers have purchased in the past areindicative of the products they will purchase in the future. This is whatrecommender systems exploit by learning models from purchase information topredict the items a customer might be interested in. We propose TransRev, anapproach to the product recommendation problem that integrates ideas fromrecommender systems, sentiment analysis, and multi-relational learning into ajoint learning objective. TransRev learns vector representations for users,items, and reviews. The embedding of a review is learned such that (a) itperforms well as input feature of a regression model for sentiment prediction;and (b) it always translates the reviewer embedding to the embedding of thereviewed items. This allows TransRev to approximate a review embedding at testtime as the difference of the embedding of each item and the user embedding.The approximated review embedding is then used with the regression model topredict the review score for each item. TransRev outperforms state of the artrecommender systems on a large number of benchmark data sets. Moreover, it isable to retrieve, for each user and item, the review text from the training setwhose embedding is most similar to the approximated review embedding.

TransRev: Modeling Reviews as Translations from Users to Items