# Understanding Query Combination Behavior in Exploratory Searches
## 1 Introduction  
Exploratory search is a special type of information seeking [1] carried out by search engine users who are not familiar with their search domains, who are motivated by complex information needs, or who encounter indexes of information which are inadequate [2; 3; 4]. Under such conditions, users have to spend more time searching for related documents, obtaining new knowledge from them, and planning for new queries by synthesizing the information they gathered during the search process [5; 6]. Exploratory search studies focus on diversifying user search behaviors to help users learn and explore the unknown [7; 8; 9]. During the process of analyzing user behavior in exploratory searches, there is a wealth of search behavior where users combine two or more issued queries into new queries. In this work, we present such a type of search behavior as query combination behavior (QCB). Here, we consider two examples to illustrate this behavior. Both examples come from the AOL data set (released by America Online, a collection of real query logs that is based on 650,000 real users). Figure 0(a) shows a search session where the user is seeking information on dogs. We can see that, in this session, the user issued content-related queries (all queries pertaining to dogs) and clicked on relevant results. The descriptions of information needs are becoming more and more accurate (\"pitbull\" is a kind of dog, but the query \"pitbull\" yields results on a variety of topics, such as the singer \"[PERSON]\" and the nickname \"[PERSON]\"; therefore, the query \"pitbull dogs\" is a more accurate description of the information needs compared with the query \"pitbull\", thereby reducing redundant topics), providing some evidence that the user is exploring during information seeking. The user performed QCB by combining the query \"dogs\" and the query \"pitbull\" into the following query: \"pitbull dogs\". Figure 1b shows another search session, where the user was seeking musical resources. The query \"everything I want the world\" and the query \"a pleasing sense of happiness\" are both lyrics of a song named \"Top of the world\", which was evidence that the user was exploring information pertaining to this song. The user performed QCB by combining the query \"everything I want the world\" and the query \"a pleasing sense of happiness\" into the following query: \"everything I want the world a pleasing sense of happiness for me\". We observed that users can meet their information needs by combining queries where they analyzed and synthesized the information they have learned. For example, in Figure 1a, the user performed QCB after he or she: (1) analyzed the search results (found the query \"pitbull\" yields results on a variety of topics); (2) synthesized the information needs \"pitbull\" with the topic \"dogs\". We also notice that the inter-query time between \"pitbull\" and \"pitbull dogs\" is relatively short (the click on the web page is a music website which has nothing to do with dogs), and the inter-query time between \"pitbull dogs\" and the end of the session is the longest, providing some evidence that the query \"pitbull dogs\" can better meet the user's information need. Based on this observation, the users who performed QCB have to search and learn during their information retrieval processes. Therefore, users who perform QCB not only have sufficient searching abilities but also sufficient learning abilities. The current literature on users' searching behaviors has figured out that users would be able to search better and possibly learn more with their learning skills (e.g., understanding, analysis, application, or synthesis) improved [10, 11]. These studies emphasize the importance of conceptualizing learning as search outcomes [12]. Searching as learning (SAL) studies bring together two disconnected research areas: information retrieval and pedagogy [13].