让用户主动控制搜索个性化，既精准又可调节。
Bridging Personalization and Control in Scientific Personalized Search
- 用可编辑记忆增强交叉编码器，实现高效个性化。
- 通过校准混合模型，仅在必要时才启用个性化。
- 用户研究验证了控制权对搜索体验的提升效果。
个性化搜索依赖用户历史交互数据学习偏好，以提升文档相关性。但现有方法透明度低，用户难以控制，且易导致信息茧房。为此，本文提出CtrlCE模型，基于用户历史交互构建可编辑记忆的交叉编码器，实现高效个性化与用户主动控制。针对非所有查询都需个性化的问题，引入校准混合模型，自动判断何时启用个性化。在四个科学领域进行实证评估，验证了模型性能；校准实验显示可选择性触发控制；用户研究证明可编辑记忆有效提升了控制能力。
原文摘要 · Abstract (English)
Personalized search is a problem where models benefit from learning user preferences from per-user historical interaction data. The inferred preferences enable personalized ranking models to improve the relevance of documents for users. However, personalization is also seen as opaque in its use of historical interactions and is not amenable to users' control. Further, personalization limits the diversity of information users are exposed to. While search results may be automatically diversified this does little to address the lack of control over personalization. In response, we introduce a model for personalized search that enables users to control personalized rankings proactively. Our model, CtrlCE, is a novel cross-encoder model augmented with an editable memory built from users' historical interactions. The editable memory allows cross-encoders to be personalized efficiently and enables users to control personalized ranking. Next, because all queries do not require personalization, we introduce a calibrated mixing model which determines when personalization is necessary. This enables users to control personalization via their editable memory only when necessary. To thoroughly evaluate CtrlCE, we demonstrate its empirical performance in four domains of science, its ability to selectively request user control in a calibration evaluation of the mixing model, and the control provided by its editable memory in a user study.
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