Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence Hierarchical Bayesian Matrix Factorization with Side Information

Citations in the corpus

• Modeling Consumer Preferences and Price Sensitivities from Large-Scale Grocery Shopping Transaction Logs (2017, methodology): “Of particular interest, feature-based matrix factorization techniques have been proposed [3,4,27,28,30] and efficient tools (e.”
• Improving Content-based and Hybrid Music Recommendation using Deep Learning (2014, methodology): “Although such approaches can potentially generalize to music recommendation, we do not use them due to efficiency issues: (1) they use full Bayesian inference [20, 22, 23] or Monte Carlo simulation [24] and are thus much slower than our algorithm; (2) they have been applied to a dataset with thousan”
• Deep Collaborative Filtering via Marginalized Denoising Auto-encoder (2015, methodology): “A variational BMF method and a hierarchical BMF method that utilizes side information were also proposed in [27] and [28], respectively.”