社会化阅读平台的信息查询提示机制优化
Optimization Study of Query Suggestion of Social Reading Network Based on Users’ Behavior Feature Library
为克服检索过程中的信息过载问题，提出一种查询提示优化方法，用于优化社会化阅读平台的查询提示效果。首先，利用基于查询词标准化共现矩阵来构建用户行为特征库，并将这种行为库融入社会化阅读平台查询提示模块，结合其它历史检索信息来优化查询提示列表的提示效果。随后,通过模拟查询提示过程，分别对提示效果的丰富度和检全率进行量化计算。实验结果表明：提示结果在检全率和丰富性方面表现较好，对学习型用户来说其提示结果具有较好的预测性，进而从信息融合角度可更好地提升学习型用户的检索体验。
This paper aims to improve the user searching experience by optimizing the query suggestions on the social reading network. First, we build users’ behavior feature library by utilizing the standardized query co-occurrence matrix. Then implement this procedure into social reading network to enhance the effective of query suggestions list. In the experimental study, we simulated the operation process of query suggestions. The results shows this method can enhance the effective of query suggestions in both richness and detection rate perspectives, the forecasting function also performed for expert users.
程秀峰、严中华、孟亚琪
华中师范大学信息管理学院华中师范大学国家数字化学习工程技术研究中心华中师范大学信息管理学院
科学交流与知识传播
社会化阅读平台查询提示用户行为优化研究
Social Reading NetworkQuery SuggestionUsers’ BehaviorOptimization Study
程秀峰,严中华,孟亚琪.社会化阅读平台的信息查询提示机制优化[EB/OL].(2022-07-07)[2024-12-22].https://sinoxiv.napstic.cn/article/3444820.点此复制
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