Scholarlink

<p>Contextual recommendation has become attainable through the massive amounts of contextual information generated by smartphones and Internet of Things (IoT) devices. The availability of a huge amount of contextual data paves the way for a revolution in recommendation systems. It overcomes the static nature of personalization, which does not allow the discovery of new items and interests, toward a contextualization of the user’s tastes, which are in constant evolution. In this paper, we proposed CoD-MaF, a Context-Driven Collaborative Filtering using Contextual biased Matrix Factorization. Our approach employs feature selection methods to extract the most influential contextual features that will be used to cluster the users using K-means algorithm. The model then performs a collaborative filtering based on matrix factorization with improved contextual biases to suggest relevant personalized recommendations. We highlighted the performance of our method through experiments on four datasets (LDOS-CoMoDa, STS-Travel, IncarMusic and Frappe). Our model enhanced the accuracy of predictions and achieved competitive performance compared to baseline methods in metrics of RMSE and MAE.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

CoD-MaF: toward a Context-Driven Collaborative Filtering using Contextual Biased Matrix Factorization

  • 期刊论文

  • 发表日期：

    2025年3月25日

查看全文
  • Jihene Latrech,
  • Zahra Kodia,
  • Nadia Ben Azzouna

摘要

Contextual recommendation has become attainable through the massive amounts of contextual information generated by smartphones and Internet of Things (IoT) devices. The availability of a huge amount of contextual data paves the way for a revolution in recommendation systems. It overcomes the static nature of personalization, which does not allow the discovery of new items and interests, toward a contextualization of the user’s tastes, which are in constant evolution. In this paper, we proposed CoD-MaF, a Context-Driven Collaborative Filtering using Contextual biased Matrix Factorization. Our approach employs feature selection methods to extract the most influential contextual features that will be used to cluster the users using K-means algorithm. The model then performs a collaborative filtering based on matrix factorization with improved contextual biases to suggest relevant personalized recommendations. We highlighted the performance of our method through experiments on four datasets (LDOS-CoMoDa, STS-Travel, IncarMusic and Frappe). Our model enhanced the accuracy of predictions and achieved competitive performance compared to baseline methods in metrics of RMSE and MAE.