用户可自主解决推荐冷启动问题，无需平台配合
Solving the Cold Start Problem on One's Own as an End User via Preference Transfer
- 通过迁移偏好构建用户自适应推荐算法
- 在真实数据集上显著提升冷启动用户推荐效果
- 适用于无平台支持的独立用户场景
我们提出一种新方法，使终端用户能自主解决推荐系统中的冷启动问题。该问题在推荐系统中普遍存在，以往多由服务方采取措施应对，但当服务方未行动时，用户只能接受劣质推荐。为此，我们设计了算法Pretender，使用户可独立部署并主动优化自身推荐体验，无需服务方支持。该方法将问题建模为源分布与目标分布间距离最小化，并据此优化目标服务中的物品选择。同时，我们基于离散求积问题建立了Pretender的理论保证。实验在真实数据集上验证了其有效性。
原文摘要 · Abstract (English)
We propose a new approach that enables end users to directly solve the cold start problem by themselves. The cold start problem is a common issue in recommender systems, and many methods have been proposed to address the problem on the service provider's side. However, when the service provider does not take action, users are left with poor recommendations and no means to improve their experience. We propose an algorithm, Pretender, that allows end users to proactively solve the cold start problem on their own. Pretender does not require any special support from the service provider and can be deployed independently by users. We formulate the problem as minimizing the distance between the source and target distributions and optimize item selection from the target service accordingly. Furthermore, we establish theoretical guarantees for Pretender based on a discrete quadrature problem. We conduct experiments on real-world datasets to demonstrate the effectiveness of Pretender.
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