Unveiling Superiority: Evaluating Bernoulli Matrix Factorization in Recommender Systems with Ciao Dataset Dominance

This paper examines the complex landscape of recommender systems, focusing in particular on the effectiveness of Bernoulli Matrix Factorization (BeMF). The performance of BeMF is systematically assessed against renowned state-of-the-art models, TrustEV, GCFA, SBRNE, RAWATD, and PMF, utilizing a diverse array of datasets, including the widely used Ciao dataset. evaluation, centered on the critical metric of Mean Absolute Error (MAE), consistently reveals the superior accuracy and proficiency of our BeMF model, notably excelling on the Ciao dataset. This thorough examination encompasses various dimensions, encompassing user preferences, social trust, behavior integration, and innovative trust synthesis. Contributing to the ongoing discourse in recommender system research, this study illustrates Bernoulli Matrix Factorization s versatility and potency, highlighting its ability to improve recommendation accuracy and adaptability in varied scenarios.