A Wallboard Outsourcing Recommendation Method Based on Dual-Channel Neural Networks and Probabilistic Matrix Factorization Article Swipe

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Dual (grammatical number) Outsourcing Factorization Probabilistic logic Artificial neural network Matrix (chemical analysis) Non-negative matrix factorization Matrix decomposition Computer science Channel (broadcasting) Artificial intelligence Algorithm Telecommunications Business Materials science Physics Composite material Literature Art Marketing Eigenvalues and eigenvectors Quantum mechanics

Hongxin Yang , Shanhui Liu , Y. Cao , Yuanyang Wang , Chaoyang Li ·

YOU? · · 2025 · Open Access · · DOI: https://doi.org/10.3390/electronics14142792 · OA: W4412194258

Wallboard outsourcing is a critical task in cloud-based manufacturing, where demand enterprises seek suitable suppliers for machining services through online platforms. However, the recommendation process faces significant challenges, including sparse rating data, unstructured textual descriptions from suppliers, and complex, non-linear user preferences. To address these issues, this paper proposes AttVAE-PMF, a novel recommendation method based on dual-channel neural networks and probabilistic matrix factorization. Specifically, an attention-enhanced long short-term memory (LSTM) is employed to extract semantic features from free-text supplier descriptions, while a variational autoencoder (VAE) is used to model latent preferences from sparse demand-side ratings. These two types of latent representations are then fused via probabilistic matrix factorization (PMF) to complete the rating matrix and infer enterprise preferences. Experiments conducted on both the wallboard dataset and the MovieLens-100K dataset demonstrate that AttVAE-PMF outperforms baseline methods—including PMF, DLCRS, and SSAERec—in terms of convergence speed and robustness to data sparsity, validating its effectiveness in handling sparse and heterogeneous information in wallboard outsourcing recommendation scenarios.