Deep Learning-Driven Multi-Criteria Decision-Making for Effective Recommender Systems

Abstract

Recommender systems are extensively employed across various domains, encompassing movies, hotels, and restaurants, with the aim of delivering personalized recommendations to users.However, the conventional approach of relying solely on a single rating may fall short in comprehending the underlying factors contributing to a user's overall rating.As a remedy, multi-criteria recommender systems (MCRSs) have emerged as an alternative paradigm, allowing users to rate items based on multiple dimensions.The primary challenge lies in predicting the overall rating considering a user's multi-criteria scores.In this study, we propose a MCRS that leverages a multicriteria decision-making method, which takes into account the interdependence among criteria.To this end, the weight assigned to each criterion is calculated using the correlation coefficient and standard deviation (CCSD) approach.Furthermore, we employ a deep autoencoder to capture intricate nonlinear relationships and generate recommendations of heightened accuracy.Evaluating our proposed method on the well-known MovieLens and TripAdvisor datasets, we demonstrate its superior performance compared to several single and multicriteria recommender systems.The results highlight the effectiveness of our approach in capturing pertinent aspects and achieving lower prediction errors, showcasing its potential in enhancing recommendation quality and personalization.