One fundamental idea behind this approach is that there exist patterns among different users’ preferences. And we propose a linear regression model to characterize the inner relationships …

Aug 14, 2023 · The simple linear regression approach. We approach the recommendation engine as a linear regression problem. We can formalize the problem in a linear regression model, y = …

Dec 15, 2017 · We propose a content-based filtering algorithm based on a multiattribute network. Network analysis can consider similarities among indirectly-connected items. The proposed …

SGD is the main training algorithm for many current machine learning methods including deep learning. The key advantage of LMS is that it can be used on-line and used adaptively. Each …

May 6, 2025 · In this work, we focus on using linear filtering for regression in streaming data. Linear filtering allows us to build efficient and accurate proactive general models without the …

May 8, 2024 · In a typical linear regression, the X values are predefined or calculated before the model is trained. In contrast, in collaborative filtering, the X values are learned alongside the …

Abstract: A personalized collaborative filtering recommendation algorithm based on a linear regression model. Constructing the linear regression model based on the user label weight …

Feb 12, 2018 · In this paper, from the perspective of regression, we propose a cross-domain collaborative filtering algorithm based on Feature Construction and Locally Weighted Linear …

To analyze some of the multiple linear regression methodologies, including Linear, Ridge, Lasso, Random Forest Regression, and XGBoost Regression. Altogether, this paper provides an …

May 6, 2025 · In this paper, we present a novel approach to the problem based on classical linear adaptive filtering theory. The linearity allows to obtain simple general models highly accurate …