论文标题
基于知识差距的相关模型
Relevance Models Based on the Knowledge Gap
论文作者
论文摘要
搜索系统越来越多地用于通过从大量内容中访问相关资源来获得知识。但是，搜索系统仅为知识获取环境中的用户提供有限的支持。具体来说，他们没有完全考虑我们将知识差距定义为用户所知道的内容与用户打算学习的内容之间存在的差距。在搜索系统中，考虑知识获取任务的知识差距在很大程度上仍未探索的影响。我们建议将知识差距建模并纳入搜索算法。我们计划探索知识差距在多大程度上导致搜索系统在知识获取任务中的性能的改善。此外，我们旨在调查和设计一个指标，以评估搜索系统在知识获取任务的背景下。
Search systems are increasingly used for gaining knowledge through accessing relevant resources from a vast volume of content. However, search systems provide only limited support to users in knowledge acquisition contexts. Specifically, they do not fully consider the knowledge gap which we define as the gap existing between what the user knows and what the user intends to learn. The effects of considering the knowledge gap for knowledge acquisition tasks remain largely unexplored in search systems. We propose to model and incorporate the knowledge gap into search algorithms. We plan to explore to what extent the incorporation of the knowledge gap leads to an improvement in the performance of search systems in knowledge acquisition tasks. Furthermore, we aim to investigate and design a metric for the evaluation of the search systems' performance in the context of knowledge acquisition tasks.
