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Scaling Accuracy in Sparse Environments: Empirical Validation of Semantic-Enriched Probabilistic Matrix Factorization" by Jaeni AMIKOM is a document available to read on EtoBox.

What is Scaling Accuracy in Sparse Environments: Empirical Validation of Semantic-Enriched Probabilistic Matrix Factorization" about?

The document presents SEMAR-LF, a hybrid recommendation model that combines Probabilistic Matrix Factorization, LSTM networks, and semantic embeddings to address data sparsity in e-commerce systems. Experimental results show that SEMAR-LF significantly outperforms traditional models, achieving up to a 44.62% improvement in RMSE on sparse datasets. The study highlights the model

Author

Jaeni AMIKOM

Language

EN