Expressive latent feature modelling for explainable matrix factorisation-based recommender systems
journal contributionposted on 2022-08-05, 14:26 authored by Abdullah Alhejaili, Syeda FatimaSyeda Fatima
The traditional matrix factorisation (MF) based recommender system methods, despite their success in making the recommendation, lack explainable recommendations as the produced latent features are meaningless and cannot explain the recommendation. This paper introduces an MF-based explainable recommender system framework that utilises the user-item rating data and the available item information to model meaningful user and item latent features. These features are exploited to enhance the rating prediction accuracy and the recommendation explainability. Our proposed feature-based explainable recommender system framework utilises these meaningful user and item latent features to explain the recommendation without relying on private or outer data. The recommendations are explained to the user using text message and bar chart. Our proposed model has been evaluated in terms of the rating prediction accuracy and the reasonableness of the explanation using six real-world benchmark datasets for movies, books, video games and fashion recommendation systems. The results show that the proposed model can produce accurate explainable recommendations.
- Computer Science
Published inACM Transactions on Interactive Intelligent Systems
Pages1 - 30
PublisherAssociation for Computing Machinery
- AM (Accepted Manuscript)
Rights holder© the owner/authors
Publisher statement© the owner/authors 2022. This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in ACM Transactions on Interactive Intelligent Systems, http://dx.doi.org/10.1145/3530299.