A hybrid multi-criteria recommendation algorithm based on autoencoders
Zeynep Batmaz, Cihan Kaleli · Pamukkale University Journal of Engineering Sciences · 2024
Multi-criteria recommender systems provide efficient solutions to deal with information overload problem by producing personalized recommendations considering multiple criteria. Even though multi-criteria recommender systems provide more accurate and personalized recommendations to their users compared with traditional recommender systems, sparsity becomes a major problem for such systems due to the increasing number of criteria. Due to the lack of co-rated items among users, finding out neighbors and producing accurate predictions become harder. Especially similarity-based multi-criteria recommendation approaches are significantly affected by the sparsity problem. Thus, aiming to minimize the negative impacts of that problem, a hybrid similarity-based multi-criteria recommendation method, that utilizes complex, low-dimensional and latent features obtained from both reviews and criteria ratings by autoencoders, is proposed in this work. The empirical results performed on a real data set with a sparsity percentage of 99.7235% show that the proposed work can provide more accurate predictions compared with other neighborhood-based multi-criteria approaches.