论文标题
关于自然语言用户资料，以透明和可仔细的建议
On Natural Language User Profiles for Transparent and Scrutable Recommendation
论文作者
论文摘要
近年来，与建议和个性化搜索系统的自然互动受到了极大的关注。我们专注于支持人们对这些系统的理解和控制，并探索一种关于建议和个性化系统中知识代表的新思维方式的挑战。具体而言，我们认为，对于使用要开发用户偏好的自然语言表示的算法可能是可取的，并且可能是可能的。我们认为这可以提供更大的透明度，以及对建议和控制建议的实际审讯和控制的能力。此外，我们认为，这种方法（如果成功地采用）可以迈出迈向较少依赖嘈杂的隐式观察的系统，同时增加对自己利益的知识的可移植性。
Natural interaction with recommendation and personalized search systems has received tremendous attention in recent years. We focus on the challenge of supporting people's understanding and control of these systems and explore a fundamentally new way of thinking about representation of knowledge in recommendation and personalization systems. Specifically, we argue that it may be both desirable and possible for algorithms that use natural language representations of users' preferences to be developed. We make the case that this could provide significantly greater transparency, as well as affordances for practical actionable interrogation of, and control over, recommendations. Moreover, we argue that such an approach, if successfully applied, may enable a major step towards systems that rely less on noisy implicit observations while increasing portability of knowledge of one's interests.
