Incremental trust-aware matrix factorization for recommender systems: towards Green AI.

Developing machine learning models that outperform the existing ones has been defined as the main goal of researchers and industry experts. In this context, the carbon footprint caused by conducting extended experiments on huge datasets for fine tuning the model parameters is usually ignored. Here, we present an efficient hybrid trust-aware incremental recommendation system that is based on factorization of user-item matrices. The method uses trust networks of the customers obtained from social networks. The main advantage of the method is that it is incremental and there is no need to re-train the system from scratch in presence of new customer activities and only newly added users and items are learned. To tackle the sparseness of the matrix, known as cold-start problem, we employed a deep neural network to decrease the user ratings approximation error. Moreover, we evaluated three different initialization methods for matrix factorization algorithm. The method is evaluated on two versions of popular MovieLens dataset from GroupLens research and also Extended Epinions dataset. The experimental results confirm that our method significantly requires less computational resources, while providing comparable prediction error. [ABSTRACT FROM AUTHOR]