# Understanding Query Combination Behavior in Exploratory Searches
## 2 Related Work  
Characterizing the query behavior of exploratory search users has been the subject of previous studies from different perspectives. The line of work most related to ours is query reformulation, because QCB can be seen as a typical kind of query reformulation in exploratory searches.  
Query reformulation has been widely studied to reduce the gap between queries and information needs [15; 16]. By reformulating user queries into appropriate new forms, it enables users to retrieve more relevant information [17]. Therefore, studies of query reformulation are particularly important in exploratory search, as users may lack the necessary domain knowledge to describe their information needs [3; 18; 19].  
Some research studied query reformulation from the perspective of reformulation strategies. [PERSON] et al. [20] focus on the taxonomy developed by analyzing search logs and summarizing query reformulation strategies from prior works. These strategies include adding words, removing words, word reorder, word substitution, etc. [PERSON] et al. [21] classify user query reformulations into four categories, as follows: generalization, specialization, error correction, and parallel move. They built a model for classifying query reformulations. [PERSON] et al. [22] proposed that the studies on query reformulation can be divided into three categories, as follows: query reduction, substitution, and query expansion. While these studies serve as insights into query re-formulation, these approaches all focus on superficial lexical aspects of reformulation [20].  
Web search involves abundant human interactions, and these activity footprints can reflect the real information needs of the user [23; 24; 25]. A number of recent studies have shown that log-based user behaviors provide rich context for performing query reformulation [26]. To better understand user reformulations, recently, more studies have focused on analyzing users' reformulating behaviors [3; 15]. Therefore, search logs are widely used to analyze user reformulating behavior because they record information about the full interaction between users and search engines [27].  
[PERSON] et al. [3] investigated the use of query reformulation in exploratory search and explore the effect of query reformulation on user behavior. They found that query reformulation suggestions are important in the decision-making process of whether or not to terminate the current phase of their information seeking. [PERSON] et al. [28] proposed an exploitation of heterogeneous network embedding to learn the semantics of queries and term the dependencies of queries in search logs. They figured out that homomorphic reformulations embeddings that precisely capture both syntactic and semantic reformulations can essentially benefit query reformulation suggestion. [PERSON] et al. [29] exploredthe relationship between terms in adjacent queries to aid in query reformulation. They indicated that a significant number of added terms in query reformulations can be sourced from the terms that the user was exposed to in the previous impression.  
This work extends existing research in the following ways: no previous study on exploratory search examines how users combine queries and why they perform QCB. We summarize a hierarchy of this kind of search behavior and employ log analysis to provide new insight into combination success. We characterize differences in the search behavior associated with different types of QCB in the hierarchy and develop a classifier based on these differences to automatically classify QCB for a given search log.