This repository contains the code and resources for building a personalized movie recommendation system using matrix factorization, incorporating both latent factors and bias terms. The model is ...

Abstract: Matrix factorization is a fundamental characterization model in machine learning and is usually solved using mathematical decomposition reconstruction loss. However, matrix factorization is ...

Matrix factorization is a powerful technique for analyzing large datasets and extracting latent features. This technology is utilized in a wide range of fields, including recommendation systems, image ...

2022_PR paper Efficient federated multi-view learning code $\min_{{U^{(m)},V^{(m)}},V} \quad\sum_{m=1}^{M}\alpha^{(m)} | X^{(m)}-U^{(m)}(V^{(m)})^{\top}|_{F}^{2 ...

Collaborative filtering generates recommendations by exploiting user-item similarities based on rating data, which often contains numerous unrated items. To predict scores for unrated items, matrix ...

Abstract: In this paper, we consider the problem of on-demand source coding for collaborative data dissemination in vehicular ad-hoc networks (VANETs). Specifically, we address the problem of index ...