社会化阅读平台的信息查询提示机制优化

Optimization of Query Suggestion of Social Reading Network Based on User 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,利用基于查询词的标准化共现矩阵来构建用户行为特征库,并将这种特征库融入社会化阅读平台查询提示模块,结合其他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 user searching experience by optimizing the query suggestions on social reading network. Firstly, a user behavior feature database (library) is constructed by using standardized co- occurrence matrix based on query words;this behavior library is integrated into the socialized reading platform query suggestions list, from which other historical searching information is introduced to optimize the effect of the query suggestions. Then, through the simulation of query prompt process, the richness and the recall rate of prompt effect are quantified separately. The results show the method can enhance the effectiveness of query suggestions in both recall rate and richness. For expert users, the suggestion results are better predictive, and the searching experience can be better improved from the perspective of information fusion.