چکیده مقاله
This paper examines the complex landscape of recommender systems, focusing in particularon the effectiveness of Bernoulli Matrix Factorization BeMF The performance of BeMF issystematically assessed against renowned state of the art models, TrustEV, GCFA, SBRNE,RAWATD, and PMF, utilizing a diverse array of datasets, including the widely used Ciaodataset evaluation, centered on the critical metric of Mean Absolute Error MAE , consistentlyreveals the superior accuracy and proficiency of our BeMF model, notably excelling on theCiao dataset This thorough examination encompasses various dimensions, encompassing userpreferences, social trust, behavior integration, and innovative trust synthesis Contributing tothe ongoing discourse in recommender system research, this study illustrates Bernoulli MatrixFactorization's versatility and potency, highlighting its ability to improve recommendationaccuracy and adaptability in varied scenarios
کلیدواژهها
نویسندگان
Hossein Pirhadi
University of Tehran
Alireza Moumivand
Khatam University
Rooholah Abedian
University of Tehran
Amin Ghodousian
University of Tehran
شیوه ارجاع
Pirhadi, Hossein and Moumivand, Alireza and Abedian, Rooholah and Ghodousian, Amin,1402,Unveiling Superiority: Evaluating Bernoulli Matrix Factorization in Recommender Systems with Ciao Dataset Dominance,5th International Conference on Software Computing,Rudsar
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