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 leveraging 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.

中文翻译：

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通过属性驱动的主动学习解决项目冷启动问题

在推荐系统中，冷启动问题是指某些用户或项目之前没有已知事件（例如评分）的情况。在本文中，我们专注于项目冷启动问题。内容信息（例如，项目属性）和初始用户评级对于获取用户对新项目的偏好都很有价值。然而，以前的项目冷启动问题的方法要么（1）将内容信息合并到协同过滤中进行混合推荐，要么（2）主动选择用户对新项目进行评分而不考虑内容信息，然后进行协同过滤。在本文中，我们通过利用主动学习和项目的属性信息，为项目冷启动问题提出了一种新颖的推荐方案。具体来说，我们根据物品的属性和用户的评分历史设计有用的用户选择标准，并将这些标准组合在一个优化框架中来选择用户。通过利用反馈评分、用户以前的评分和项目的属性，我们然后为其他未选择的用户生成准确的评分预测。在两个真实世界数据集上的实验结果表明，我们提出的方法优于传统方法。