In recommender systems, cold-start issues are situations where no previous
events, e.g. ratings, are known for certain users or items. In this paper, we
focus on the item cold-start problem. Both content information (e.g. item
attributes) and initial user ratings are valuable for seizing users'
preferences on a new item. However, previous methods for the item cold-start
problem either 1) incorporate content information into collaborative filtering
to perform hybrid recommendation, or 2) actively select users to rate the new
item without considering content information and then do collaborative
filtering. In this paper, we propose a novel recommendation scheme for the item
cold-start problem by leverage both active learning and items' attribute
information. Specifically, we design useful user selection criteria based on
items' attributes and users' rating history, and combine the criteria in an
optimization framework for selecting users. By exploiting the feedback ratings,
users' previous ratings and items' attributes, we then generate accurate rating
predictions for the other unselected users. Experimental results on two
real-world datasets show the superiority of our proposed method over
traditional methods.

在本篇论文中，我们提出了一种结合活跃学习与商品属性信息的新型冷启动推荐方法，该方法基于商品属性和用户的评价历史设计了有用的用户选择标准，并将该标准组合成选择用户的优化框架。通过利用反馈评分、用户先前评分和商品属性，我们为其他未被选中的用户生成准确的评分预测。实验结果表明，我们的方法优于传统方法。