GenSim : GAN based Recommendation systems for personalized matrix factorization DOI

Sanae FILALI-ZEGZOUTI,

Oumayma Banouar, Mohamed Benslimane

et al.

Research Square (Research Square), Journal Year: 2025, Volume and Issue: unknown

Published: April 25, 2025

Abstract Accurately modeling user preferences is crucial for the success of modern recommendation systems (RS). Despite recent advances in generative models RS, challenges such as limited data and complexity human behavior still persist. These issues make it difficult to generate accurate authentic profiles, which are essential providing meaningful personalized recommendations. In this paper, we introduce GenSim, a novel approach that combines Generative Adversarial Networks (GANs), Genetic Algorithms (GAs), similarity techniques overcome critical collaborative filtering (CF), sparsity intricate user-item interactions. By integrating these methods, GenSim offers robust scalable framework enhancing RS performance. A key feature its focus on matrices, selectively consider only similar users or items, rather than entire matrix. This targeted refines input during generation phase, resulting recommendations not more leading accurate, personalized, efficient Our integrates GAN with an autoencoder-based discriminator optimized generator matrix factorization, incorporating Pearson enrich process. GAs employed two phases: preprocessing refine measures fine-tuning generator’s hyperparameters optimal factorization. Extensive experiments benchmark datasets—MovieLens 1M, HetRec, LastFM—demonstrate GenSim's superior performance across Precision, MAP, NDCG, compared state-of-the-art methods. improves precision by 40% at cutoff 50 MovieLens 1M dataset, MAP 38% 5 LastFM previous works using GANs factorization systems.

Language: Английский

GenSim : GAN based Recommendation systems for personalized matrix factorization DOI

Sanae FILALI-ZEGZOUTI,

Oumayma Banouar, Mohamed Benslimane

et al.

Research Square (Research Square), Journal Year: 2025, Volume and Issue: unknown

Published: April 25, 2025

Abstract Accurately modeling user preferences is crucial for the success of modern recommendation systems (RS). Despite recent advances in generative models RS, challenges such as limited data and complexity human behavior still persist. These issues make it difficult to generate accurate authentic profiles, which are essential providing meaningful personalized recommendations. In this paper, we introduce GenSim, a novel approach that combines Generative Adversarial Networks (GANs), Genetic Algorithms (GAs), similarity techniques overcome critical collaborative filtering (CF), sparsity intricate user-item interactions. By integrating these methods, GenSim offers robust scalable framework enhancing RS performance. A key feature its focus on matrices, selectively consider only similar users or items, rather than entire matrix. This targeted refines input during generation phase, resulting recommendations not more leading accurate, personalized, efficient Our integrates GAN with an autoencoder-based discriminator optimized generator matrix factorization, incorporating Pearson enrich process. GAs employed two phases: preprocessing refine measures fine-tuning generator’s hyperparameters optimal factorization. Extensive experiments benchmark datasets—MovieLens 1M, HetRec, LastFM—demonstrate GenSim's superior performance across Precision, MAP, NDCG, compared state-of-the-art methods. improves precision by 40% at cutoff 50 MovieLens 1M dataset, MAP 38% 5 LastFM previous works using GANs factorization systems.

Language: Английский

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