# Understanding Query Combination Behavior in Exploratory Searches
## 6 Discussion and Conclusions  
Exploratory search users have to seek with more effort to meet their information needs [61]. Therefore, exploratory search studies aim to help users find their required information with minimal efforts [62]. QCB is a special type of query reformulation behavior. Query reformulation studies focus on studying the users who modify their initial or previous queries, based on the judgment of search results. Previous work mainly focuses on (i) exploring the inspiration of search results to users [29]; (ii) exploring the regularity of query reformulation in historical data [63]. However, previous studies neglected to explore the relationship between the issued queries and the reformulate queries. We believe that exploring these relationships is helpful for understanding how users synthesize information. Our work is differentiated from previous studies because we successfully explored a typical kind of relationship where users combine issued queries into a new query; furthermore, we found the regular patterns of how users synthesize information. Our first finding was that we introduced a kind of novel exploratory search behavior where users combine two or more previously issued queries into new queries. On both the AOL and the Sogou data sets, we observed that users can easily find their required information by combining queries where they analyzed and synthesized the information they have learned. However, to the best of our knowledge, no studies have focused on this typical exploratory search behavior. Better understanding of this kind of exploratory search behavior is important for exploring how users search and learn during information retrieval processes and can further help people find information more easily. We list our main contributes as follows: (1) through analysis on millions of search logs, we have explored the space of QCB types and have described a two-layer hierarchical \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **AUC** & **Accuracy** & **F1** \\ \hline Gradient Boosting Tree & 79.23 * & 77.19 * & 78.81 * \\ Logistic Regression & 72.27 & 70.05 & 72.41 \\ SVM & 73.42 & 72.23 & 73.37 \\ C4.5 & 72.84 & 71.61 & 72.27 \\ \hline \hline \end{tabular}
* indicates statistical significance at \(p<0.05\) using paired \(t\)-tests compared to baseline.  
\end{table} 
Table 4: Performance of QCB sessions classification. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **AUC** & **Accuracy** & **F1** \\ \hline Dominative query first & 81.21 & 77.47 & 78.94 \\ Topic specialization query first & 81.34 & 77.21 & 70.11 \\ Similarity-oriented pattern & 82.15 & 77.91 & 79.59 \\ Distinction-oriented pattern & 79.89 & 77.14 & 78.21 \\ Diversification mining & 77.67 & 74.38 & 76.02 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Performance of QCB sessions’ classification. structure to better understand the nature of QCB; (2) through manual classification of QCB sessions, we have proposed that users can find their required information easily by performing QCB, and we further found that prompting users to perform QCB can also help them find their required information easily; (3) we characterized aspects of how users combine queries, where we found some statically significant differences to support the manual classification of QCB patterns; (4) based on these characteristics, we developed a classifier to accurately predict QCB types, with a view to helping systems understand what web searchers really want. Our research supports further analysis of different QCB patterns. Ideally, a search engine would interpret the behavioral signals that indicate QCB, accurately classify QCB types, and provide personalized help to searchers to help them attain task success. For example, through the following possibilities:  
Enhance understanding of the constructs of learning in searching: [PERSON] and [PERSON] [10] proposed that providing users with a high-quality result list is not enough--it is important to distinguish different kinds of constructs of learning in searching.