Alex Olieman

Telling How to Narrow it Down: Browsing Path Recommendation for Exploratory Search

Supporting exploratory search tasks with the help of structured data is an effective way to go beyond keyword search, as it provides an overview of the data, enables users to zoom in on their intent, and provides assistance during their navigation trails. However, finding a good starting point for a search episode in the given structure can still pose a considerable challenge, as users tend to be unfamiliar with exact, complex hierarchical structure. Thus, providing lookahead clues can be of great help and allow users to make better decisions on their search trajectory. In this paper, we investigate the behaviour of users when a recommendation engine is employed along with the browsing tool in an exploratory search system. We make use of an exploratory search system that facilitates browsing by mapping the data on a hierarchical structure. We designed and developed a path recommendation engine as a feature for this system, which given a text query, ranks different browsing paths in the hierarchy based on their likelihood of covering relevant documents. We conduct a user study comparing the baseline system with the featured system. Our main findings are as follows: We observe that, using the baseline system the users tend to explore the data in a breadth-first-like approach by visiting different data points at the same level of abstraction to choose one of them to expand and go deeper. Conversely, with browsing path recommendation (BPR) as a feature, the users tend to drive their search in a more depth-first-like approach by quickly going deep into the data hierarchy. While the users still incline to explore different parts of the search space by using BPR, they are able to restrain or augment their search focus more quickly and access smaller but more promising regions of the data. Therefore, they can complete their tasks with less time and effort

On Search Powered Navigation

main components of exploratory search. Search lets users dig in deep by controlling their actions to focus on and find just the information they need, whereas navigation helps them to get an overview to decide which content is most important. In this paper, we introduce the concept of search powered navigation and investigate the effect of empowering navigation with search functionality on information seeking behavior of users and their experience by conducting a user study on exploratory search tasks, differentiated by different types of information needs. Our main findings are as follows: First, we observe radically different search tactics. Using search, users are able to control and augment their search focus, hence they explore the data in a depth-first, bottom-up manner. Conversely, using pure navigation they tend to check different options to be able to decide on their path into the data, which corresponds to a breadth-first, top-down exploration. Second, we observe a general natural tendency to combine aspects of search and navigation, however, our experiments show that the search functionality is essential to solve exploratory search tasks that require finding documents related to a narrowdomain. Third, we observe a natural need for search powered navigation: users using a system without search functionality find creative ways to mimic searching using navigation.

Mastery Profiling Through Entity Linking

Computer-supported group formation enables educators to assign students to project teams. The focus in this paper is placed on gathering data about student attributes that are relevant in the context of specific course projects. We developed a method that automatically produces learner models from existing documents, by linking students to topics and estimating the levels of skill, knowledge, and interest that students have in these topics. The method is evaluated in an experiment with student participants, wherein its performance is measured on two levels. Our results demonstrate that it is possible to link students to topics with high precision, but suggest that estimating mastery levels is a more challenging task.