Nonparametric bayesian multitask collaborative filtering

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Recurrent Latent Variable Networks for Session-Based Recommendation (2017, methodology): “To this end, we draw inspiration from recent RS developments which rely on the utilization of Bayesian inference techniques (e.g., [5, 6, 12, 22, 27]).”
Position-Transitional Particle Swarm Optimization-Incorporated Latent Factor Analysis (2022, methodology): “Representative ones include a probabilistic MF model [16], a multi ‐ dimensional probabilistic model [17], an SVD++ [18], a non ‐ parametric Bayesian ‐ based latent factor model [19], and a weighted trace ‐ norm regularization ‐ based model [20].”
Compressed knowledge transfer via factorization machine for heterogeneous collaborative recommendation (2015, background): “From the perspective of ‘‘what to transfer’’ in transfer learning [19], previous works share different types of knowledge, including covariance [1], codebook [6,12,13,18], latent features [5,8,29], etc.”
Deep Collaborative Filtering via Marginalized Denoising Auto-encoder (2015, methodology): “Among various CF methods, matrix factorization has emerged as a powerful tool to perform recommendations in large datasets [2, 3].”