探寻式搜索中用户满意度状态识别与演化规律研究

Data Analysis and Knowledge Discovery Identifying User Satisfaction Levels and Evolution Patterns in Exploratory Search

【目的】识别探寻式搜索中的用户满意度状态,揭示用户满意度与用户查询式重构模式之间的相互作用关系和动态演化规律。【方法】利用用户查询、时序等特征,基于4种有监督学习算法进行用户满意度预测;通过挖掘用户满意度与查询式重构模式之间的相互影响规律,指导探寻式搜索智能辅助中的查询式重构推荐策略。【结果】在开放基准数据集上,所构建的满意度预测模型的预测准确率最高达到74%,优于已有基线模型;同时,相关性分析的结果表明用户满意度与查询式重构模式之间的关联关系显著。【局限】用户满意度仅代表搜索状态的一种视角,未来需要针对探寻式搜索中的用户状态构建完善统一的描述和分类体系。【结论】利用探寻式搜索中的用户搜索行为,优化了模型特征,进一步提升用户满意度预测模型的性能,并结合用户满意度演化规律,为探寻式搜索中的智能搜索辅助策略提供了有效的理论支撑。

[Objective]This paper identifies the user satisfaction levels in exploratory search and reveals the interaction and evolution between user satisfaction and reconstruction patterns of queries.[Methods]First,we retrieved the characteristics of user queries and their temporal sequences.Then,we used four supervised learning algorithms to predict user satisfaction levels.Third,we identified the interaction between user satisfaction and query reformulations.Finally,we developed new recommendation strategies for query reformulation in intelligent exploratory search assistance.[Results]We examined the proposed model with an open benchmark dataset,and the model’s prediction accuracy reached 74%,surpassing existing baseline models.There is a significant association between user satisfaction and query reformulation patterns.[Limitations]User satisfaction represents only one of the search perspectives.Future research should focus on constructing a comprehensive and unified description and classification system for users in exploratory search.[Conclusions]The proposed model further enhances the performance of the user satisfaction prediction.It provides theoretical support for intelligent search assistance strategy.