ReEx: An integrated architecture for preference model representation and explanation. (15th December 2020)

• Record Type: Journal Article
• Authors: Zafari, Farhad; Moser, Irene; Sellis, Timos
• Is Part Of: Expert systems with applications. Volume 161(2020)
• Publication Date: 2020-12-15
• Subjects: Explaining recommendations -- Recommender systems -- Latent factor models -- Aspects of preferences -- User and item bias -- Feature preferences -- Feature value preferences -- Preference drift; Expert systems (Computer science) -- Periodicals; Systèmes experts (Informatique) -- Périodiques; Electronic journals; 006.33
• DOI: 10.1016/j.eswa.2020.113706
• Languages: English
• ISSNs: 0957-4174

Abstract

Highlights: The reasons for recommendations are extracted from the preference aspects. The rating-based aspect contributions are matched with verbal reviews. Graphs of aspect contributions in ratings provide a visual rationale. Word clouds show the opinions in reviews that explain a recommendation. Abstract: Recommender systems based on collaborative filtering suggest items to users according to the similarity of items or similarity of preferences of other users. Latent factor models produce rather accurate predictions of user preferences, but the latency of the features extracted make it difficult to substantiate a recommendation to a user. The realisation that many aspects tend to exist in rating and social connections data, such as social influence or bias, led to the development of a component-based matrix factorisation approach in earlier work. The ability to quantify the contributions of a component to a recommendation opens up possibilities to identify reasons for recommendations which can be presented to the consumer receiving them. Reviews that accompany rating data can be analysed and correlated with latent factors to provide more detailed reasons for recommendations. This paper introduces a general comprehensive framework which supplements earlier work that models a users' preferences and makes recommendations by extracting reasons from the recommendation framework as well as the accompanying reviews. Both rating-based and review-based reasons for recommending an item are presented in a visual manner. These visualisations help the user understand the origins of the recommendations but also assist businesses in identifying properties in users and communities that open up opportunities for targeted marketing and customer management. The usefulness of the explanation tool is demonstrated with an example recommendation of four items for a user in the Yelp restaurants dataset.