We conducted a study of sensemaking in collaborative Web search using SearchTogether and found that collaborators face several challenges in making sense of information during collaborative search tasks.
Sensemaking is an important aspect of information-seeking tasks but has mostly been studied at the individual level. We conclude with some remarks about differentiating various types of information available within online profiles and implications for the design of expertise locator/recommender systems. Qualitative analysis provided further insights regarding the interpretations people form of others' expertise from digital artifacts. Multi-level regression analysis revealed that participation in social software, social connection information, and self- described expertise in the corporate directory were significantly helpful in the decision to contact someone for expertise.
Using signaling theory as a conceptual framework, we describe how certain 'signals' in various social software are hard to fake, and are thus more reliable indicators of expertise. This paper reports the results of a study conducted in a global company that used expertise search as a vehicle for exploring how people interpret a range of information available in online profiles in evaluating whom to interact with for expertise. In this context, we come to rely on digital artifacts to infer characteristics of other people. The results show that Adaptive VIBE can improve the precision and the productivity of the personalized search system while helping users to discover more diverse sets of information.Ĭontemporary work increasingly involves interacting with strangers in technology-mediated environments. We tested the effectiveness of Adaptive VIBE and investigated its strengths and weaknesses by conducting a full-scale user study. This paper proposes a specific way to integrate interactive visualization and personalized search and introduces an adaptive visualization based search system Adaptive VIBE that implements it.
We also suggest that an interactive visualization approach could offer a good ground to combine the strong sides of personalized and exploratory search approaches. We argue that the effectiveness of personalized search systems may be increased by allowing users to interact with the system and learn/investigate the problem in order to reach the final goal. As these approaches are not contradictory, we believe that they can re-enforce each other. In contrast, exploratory search capitalized on the power of human intelligence by providing users with more powerful interfaces to support the search process. Personalized search explored the power of artificial intelligence techniques to provide tailored search results according to different user interests, contexts, and tasks. Among these approaches, personalized search systems and exploratory search systems attracted many followers. To support the growing complexity of search tasks, researchers in the field of information developed and explored a range of approaches that extend the traditional ad hoc retrieval paradigm. As the volume and breadth of online information is rapidly increasing, ad hoc search systems become less and less efficient to answer information needs of modern users.