Modeling Navigation in Degree-of-Interest Trees

Modeling Navigation in Degree-of-Interest Trees Raluca Budiu (budiu@parc.com) Peter Pirolli (pirolli@parc.com)) Palo Alto Research Center, 3333 Coyote Hill Dr., Palo Alto, CA 94304 influences visual search most? Do people get distracted by irrelevant, but visually salient items? What is more impor- tant: the visual organization of the display or the words that are used? How do people decide what items are relevant? How can we estimate the relevance of words? Answers to this questions could have practical implication for interface designers, as well as for systems that leverage on user models to make relevant information more available to users. We propose DOI-ACT, a cognitive model of navigation in DOI trees. The model is implemented in ACT-R (Anderson & Lebiere, 1998) and can make a successful sequence of nav- igation choices and reach the solution of the search problem in time (and number of clicks) comparable to humans. Abstract We present an ACT-R (Anderson & Lebiere, 1998) compu- tational model of how people navigate in a degree-of-interest (DOI) tree. The model incorporates a visual salience func- tion that determines which part of the display to attend to next. The salience function uses visual features of the display (e.g., distances) and semantic features of labels (e.g., information scent). The model was compared against data from partici- pants and provided medium to strong fits to latencies and the number of nodes visited by the participants. The model shows that it is useful to distinguish between category-based versus similarity-based information scent. It also suggests that visual distance and scent may interact with one another, with scent playing a greater role at distances close to the current node in the visual focus. Keywords: Degree-of-interest trees, information scent, infor- mation visualization, computational modeling, ACT-R. Degree of Interest (DOI) Trees Introduction and Motivation Degree of interest trees (Card & Nation, 2002) are a form of focus+context visualization (Card, Mackinlay, & Schneider- man, 1999), intended to help with displaying large amounts of data. They compromise between a ”panoramic” and a ”zoom” view of the information, by using degree-of-interest calculations to decide which nodes should be visible. Fig- ure 1 shows a degree-of-interest tree that represents an onto- logical hierarchy of concepts. The ontology was taken from the Great CHI’97 Browse-Off (Mullet, Fry, & Schiano, 1997) and has about 7000 nodes. In Figure 1 the node that has been just clicked by the user is Artificial. Only nodes that are close to the node in focus are visible.Whereas the areas of the tree around the point of interest are shown in great detail, there is also some sense of the overall structure of the tree. Because more information than actually requested by the click is dis- played on the screen, the DOI trees may facilitate learning about the information hierarchy. Thus, tasks that make re- peated use of the same hierarchy may benefit most from this kind of visualization. One of the most prevalent tasks of contemporary society is information search and information browsing. According to Pew Internet Survey (2006), as of December 2006, out of the 141 million of Internet users in the US, 91% of them engage in information search activities (and they do it as often as they read email). Web pages often contain many links, and people are relatively good at finding their way through the in- formation maze. Due to their limited information processing resources, people rarely perform an exhaustive search in the whole space of alternative paths; rather they rely on heuristics to reach their information goal. These heuristics involve both visual cues taken from the display and semantic cues offered by the words on the page. In this paper, we study the nature of the search heuristics that people use when they interact with a complex, dynamic and information-rich display that changes at every click. Our work builds on two research areas: visual search and web navigation. There is a rich literature in psychology on visual search (see Wolfe, 1998 for a review), but most of it addresses simple visual displays and ignores the semantic content of the items on the screen. Since the emergence of the Web, several models of menu/web navigation have been proposed (Miller & Remington, 2004; Fu & Pirolli, in press; Blackmon, Kita- jima, & Polson, 2005), but they deal with simple navigation tasks and predict only few navigation choices (e.g., given that the user and the model have clicked on the same sequence of choices, they strive to predict the next user choice, rather than the entire sequence leading to the solution). The task that we study involves searching for a target in a particular search interface called degree-of-interest (DOI) tree (Card & Nation, 2002). The questions that we ask are: Does it matter where you place objects on the screen? What Information Scent One big component of the search heuristics used by people is information scent: when looking for a particular piece of information (target), a variety of semantic clues tell us how likely it is that the target be hidden under a particular label. These clues are referred to as ”information scent” (Pirolli & Card, 1999): when the information scent is high, people can find the target easily (e.g., Pirolli, Card, & Van Der Wege, 2000). Often it has been assumed that information scent re- flects semantic similarity. Word similarity measures based on word co-occurrence statistics — such as Pointwise Mutual In- formation (PMI) (Turney, 2001) or Latent Semantic Analysis (LSA) (Landauer & Dumais, 1997) — have been used suc-