论文标题
深度学习功能选择以寻求人口统计学推荐系统因素
Deep Learning feature selection to unhide demographic recommender systems factors
论文作者
论文摘要
从隐藏因素中提取人口统计特征是一个创新的概念，可提供多个相关的应用程序。矩阵分解模型会产生不包含语义知识的因素。本文提供了一种基于深度学习的方法：Deepunhide，能够从用户中提取人口统计信息和协作过滤推荐系统中的项目因素。该方法的核心是图像处理文献中使用的基于梯度的本地化，以突出每个分类类的代表性领域。验证实验利用两个公共数据集和当前基线。结果表明，与特征选择方法的艺术状态相比，Deepunhide对特征选择和人口统计学分类的优越性。相关和直接的应用程序包括建议说明，协作过滤中的公平性以及向用户组的建议。
Extracting demographic features from hidden factors is an innovative concept that provides multiple and relevant applications. The matrix factorization model generates factors which do not incorporate semantic knowledge. This paper provides a deep learning-based method: DeepUnHide, able to extract demographic information from the users and items factors in collaborative filtering recommender systems. The core of the proposed method is the gradient-based localization used in the image processing literature to highlight the representative areas of each classification class. Validation experiments make use of two public datasets and current baselines. Results show the superiority of DeepUnHide to make feature selection and demographic classification, compared to the state of art of feature selection methods. Relevant and direct applications include recommendations explanation, fairness in collaborative filtering and recommendation to groups of users.
