Identifying User Satisfaction Levels and Evolution Patterns in Exploratory Search
[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.
万方数据
维普
