Toward Explainable Recommendations: Generating Review Text from Multicriteria Evaluation Data
Takafumi J. Suzuki, Satoshi Oyama, Masahito Kurihara · 2018
Explaining recommendations helps users to make more accurate and effective decisions and improves system credibility and transparency. Current explainable recommender systems tend to provide fixed statements such as "customers who purchased this item also purchased....". This explanation is generated only on the basis of the purchase history of similar customers, so it does not include the preferences of customers who have purchased the item or a description of the item. Since user-generated reviews generally contain information about the reviewer's preferences and a description of the item, such reviews typically have more effect on purchase decisions. Therefore, using reviews to explain recommendations should be more useful than providing only a fixed statement explanation. Aiming to create a system that provides personalized explanations for recommendations, we have developed a recurrent neural network model that uses multicriteria evaluation data to generate reviews.