The problem of recommending new items to users (often referred to as item cold-start recommendation) remains a challenge due to the absence of users’ past preferences for these items. Item features from side information are typically leveraged to tackle the problem. Existing methods formulate regression methods, taking item features as input and user ratings as output. These methods are confronted with the issue of overfitting when item features are high-dimensional, which greatly impedes the recommendation experience. Availing of high-dimensional item features, in this work, we opt for feature selection to solve the problem of recommending top- N new items. Existing feature selection methods find a common set of features for all users, which fails to differentiate users’ preferences over item features. To personalize feature selection, we propose to select item features discriminately for different users. We study the personalization of feature selection at the level of the user or user group. We fulfill the task by proposing two embedded feature selection models. The process of personalized feature selection filters out the dimensions that are irrelevant to recommendations or unappealing to users. Experimental results on real-life datasets with high-dimensional side information reveal that the proposed method is effective in singling out features that are crucial to top- N recommendation and hence improving performance.

中文翻译：

---

具有个性化特征选择的项目冷启动推荐

由于缺乏用户过去对这些项目的偏好，向用户推荐新项目（通常称为项目冷启动推荐）的问题仍然是一个挑战。通常利用辅助信息中的项目特征来解决问题。现有方法制定回归方法，将项目特征作为输入，用户评分作为输出。当项目特征是高维时，这些方法面临过拟合的问题，这极大地阻碍了推荐体验。利用高维项目特征，在这项工作中，我们选择特征选择来解决推荐前 N 个新项目的问题。现有的特征选择方法为所有用户寻找一组共同的特征，这无法区分用户对项目特征的偏好。要个性化功能选择，我们建议为不同的用户有区别地选择项目特征。我们在用户或用户组层面研究特征选择的个性化。我们通过提出两个嵌入式特征选择模型来完成任务。个性化特征选择的过程过滤掉与推荐无关或对用户没有吸引力的维度。在具有高维辅助信息的现实数据集上的实验结果表明，所提出的方法可以有效地挑选出对 top-N 推荐至关重要的特征，从而提高性能。个性化特征选择的过程过滤掉与推荐无关或对用户没有吸引力的维度。在具有高维辅助信息的现实数据集上的实验结果表明，所提出的方法可以有效地挑选出对 top-N 推荐至关重要的特征，从而提高性能。个性化特征选择的过程过滤掉与推荐无关或对用户没有吸引力的维度。在具有高维辅助信息的真实数据集上的实验结果表明，所提出的方法可以有效地挑出对 top-N 推荐至关重要的特征，从而提高性能。