Search&Beyond, Kal Järvelin UTA, Andrew Howes UoB, Rob Capra UNC, Distinguished Speakers, HelsinCHI

Event type: 
Guest lecture
Event time: 
11.10.2016 - 14:00 - 16:00
Lecturer : 
Kal Järvelin UTA, Andrew Howes UoB, Rob Capra UNC
Auditorium XIV, 3. floor, Päärakennus, Unioninkatu 34

Search&Beyond, Distinguished Speakers, HelsinCHI

14:00 Welcome and Introduction

Giulio Jacucci, University of Helsinki 

Kalervo Järvelin, University of Tampere

14:30 Interfaces to Support Exploratory and Collaborative Search Tasks 

Rob Capra, University of North Carolina at Chapel Hill

15:00 The rational basis of sequential search for information

Andrew Howes, University of Birmingham

15:30 Concluding Remarks

Title: Interfaces to Support Exploratory and Collaborative Search Tasks
Search interfaces are used by millions of users every day and provide access to a vast array of information stored in web pages, document collections, and other data sources. The design of these interfaces mediates access to information and can influence our search processes. Search interfaces have evolved over time, but providing high-precision ranked lists of results is a primary focus of many systems. Current search engines are effective in helping users complete simple search tasks such as fact-finding, but provide less support in helping users with tasks that may involve exploration, analysis, comparison, evaluation, and collaboration.
In this talk I will present results from a series of projects conducted with colleagues to develop and evaluate innovative search interfaces to support exploratory and collaborative search tasks. Across these projects, we observed how interface components influenced users’ search behaviors and ways that users made use of contextual information displayed by the interfaces at different stages of their search processes. In my recent work, these observations helped inform the design of a novel search assistance tool that displays the search trails (paths) from previous users. The idea behind the tool is that users may benefit from seeing how someone else approached the same or similar task. Our implementation provides an interactive display with information about how another person searched, the queries they issued, results they clicked, and annotations made by the original searcher. I will report on a laboratory study that investigated factors that influence user interaction with the search trails and effects on outcome measures. Finally, I will conclude by discussing several exciting areas for future research on search interfaces.

Dr. Robert Capra is an Assistant Professor in the School of Information and Library Science at the University of North Carolina at Chapel Hill. His interests include human-computer interaction, interactive information retrieval, and personal information management. His research focuses on how people search for information in different contexts and on developing tools to support users’ search needs. He publishes regularly in top computer and information science conferences and journals and in 2016 was awarded a prestigious National Science Foundation CAREER grant.
He holds a Ph.D. in computer science from Virginia Tech and Master’s and Bachelor’s degrees in computer science from Washington University in St. Louis. At Virginia Tech, he was part of the Center for Human-Computer Interaction where he investigated multi-platform interfaces, information re-finding, and interfaces for digital libraries. Prior to Virginia Tech, he worked in corporate research and development, spending five years in the Speech and Language Technologies group at SBC Communications (now merged with AT&T Labs) where he focused on voice user interfaces, speech recognition, and natural language processing.
Dr. Capra is an active member in the HCI and information science communities. He has co-edited special issues of IEEE Computer, ACM Transactions on Information Systems (TOIS), and the journal Information Processing & Management. He has served on numerous conference program committees and in 2016, was co-chair of the newly formed ACM SIGIR Conference on Human Information Interaction and Retrieval (CHIIR).

Andrew Howes is Professor of Computer Science at University of Birmingham and Marshall Weinberg visiting professor at the University of Michigan. He has previously held academic posts at the University of Manchester, Cardiff University, Carnegie-Mellon University and the Medical Research Council, Cambridge. He is known for his work in Cognitive Science and Human-Computer Interaction and he focuses on computational rationality, that is in computational models of human behaviour that adapt to human cognitive capacities, as well as to the statistical structure of the environment. His recent book offers a general integrative framework for understanding human interaction with technology (Payne and Howes, 2013). Professor Howes is an Associate Editor at the International Journal of Human-Computer Studies and Cognitive Science journal. He has been an Associate Chair for ACM SIGCHI for a number of years and he is program chair for the Annual Meeting of the Cognitive Science Society (2017). His work has recently been funded by NASA (2015), by the US Air Force Research Laboratory (2013-2015), by the EU (SPEEDD: FP7-ICT-2013-11 2013-2017), and by the ESRC (ES/L00321X/1 2012-2014). A recent series of publications provide a start at over-turning the long held, and popular, misconception that human preferences are irrational (Howes et al, 2016). Inspired by work in machine learning, Howes and colleagues' Bayesian model of bounded optimal decision making shows when people make rational changes of preference (e.g. to a lottery with higher expected value but more risk). The work has potential applications in understanding the choices that people make with and through technology, for example, Lelis and Howes (2011). It also has the potential to provide a theoretical underpinning to recent interest in the use of information technology to drive behaviour change. Another contribution has been to show how framing the visual search problem faced by humans as a Partially Observable Markov Decision Problem can be used to explain otherwise puzzling phenomena in Human-Computer Interaction (Chen et al., 2015). Humans can only partially observe state because of the combined limitations of information visualisation technologies and the acuity of the human eye. With his colleagues, Howes's work shows how reinforcement learning methods can be used to predict the eye movement strategies deployed by users.

06.10.2016 - 20:47 Giulio Jacucci
06.10.2016 - 20:47 Giulio Jacucci