A novel missing value imputation for multi-criteria recommender systems

Abstract

As the internet continues to expand, users increasingly rely on digital platforms for activities such as movie streaming and travel planning, leading to an overwhelming amount of information. This information overload complicates the process of making efficient and informed decisions. To address this challenge, personalized recommender systems, particularly multi-criteria collaborative filtering (MCCF) models, have been developed to tailor recommendations based on detailed user evaluations across various sub-criteria. However, as the number of criteria in MCCF systems increases, the issue of data sparsity becomes more prominent, with more criteria resulting in more missing evaluations. In this study, we introduce an innovative application of the Bidirectional Encoder Representations from Transformers (BERT) model—well-known for its advances in natural language processing—to impute missing values within MCCF systems. By leveraging contextual insights from user reviews, we hypothesize that BERT can enhance the imputation process, thereby improving coverage and recommendation accuracy in MCCF models. Our experimental results indicate that BERT-based imputation significantly reduces data sparsity and enhances the accuracy and coverage of recommendations. This study underscores BERT's potential in processing linguistic data and highlights its utility in multi-criteria recommender systems. Integrating BERT with MCCF offers a promising advancement in addressing the inherent challenges of personalized recommendation systems.