The TiiS Best Paper Award
With the approval of the ACM Publications Board, in 2014 TiiS established an annual “TiiS Best Paper Award”. The award is to be granted each year for an article published in the volume for the previous year.
Awarded Article for 2023
The award went to Huimin Liu, Minsoo Choi, Dominic Kao, and Christos Mousas (Purdue University) for their article "Synthesizing Game Levels for Collaborative Gameplay in a Shared Virtual Environment", which was published in 2023, issue 13(1) of TiiS.
Awarded Article for 2022
The award went to Yuri Nakao (Fujitsu Ltd.), Simone Stumpf (University of Glasgow), Subeida Ahmed (City, University of London), Aisha Naseer (Fujitsu Research of Europe Ltd.), and Lorenzo Strappelli (City, University of London) for their article “Toward Involving End-users in Interactive Human-in-the-loop AI Fairness”, which was published in 2022, issue 12(3) of TiiS.
Awarded Article for 2020
The Best Paper Award Committee members for 2019 were Professor Joyce Chai, Professor Shixia Liu, and Professor Jean Vanderdonckt (chair).
The award went to Yongsu An and Yu-Ru Lin (University of Pittsburgh) for their article "Policy Flow: Interpreting Policy Diffusion in Context", which was published in 2020, issue 10(2) of TiiS.
The committee decision is cited as follows:
The authors introduce an interactive visual analytics system to analyze the diffusion of policies among states in the USA. The authors employ a network inference method to model the diffusion graph among states and augment the analysis with a set of hybrid visualization and interaction techniques. The tight integration of the diffusion modeling and interactive visualization techniques facilitates analysts in examining policy diffusion patterns from different perspectives. As such, this paper demonstrates the advantages of interadtive visual analytics for understanding the policy adoption and exemplifies the two key characteristics of an interactive intelligent system: intelligence and interactivity.
Awarded Article for 2019
The Best Paper Award Committee members for 2019 were Judy Kay (chair), Oliver Brdiczka, and Anbang Xu.
The award went to Fan Du, Catherine Plaisant, Neil Spring, Kenyon Crowley and Ben Shneiderman (University of Maryland) for their article EventAction: A Visual Analytics Approach to Explainable Recommendation for Event Sequences, which was published in issue 9(4) of TiiS.
The committee’s decision to single out this article was explained as follows:
The paper aims to augment analysts’ ability to find similar records, review recommended actions, and make action plans to improve outcomes. This is a task of considerable importance in our data rich world. The research exemplifies technical excellence in its design, implementation and comprehensive evaluation of a visual analytics interface supported by interactive machine learning. It seamlessly integrates intelligence with a sophisticated user interface. The authors have shown the promise of this work for high impact on the research community and beyond as the paper reports reports studies of the system in three case studies across two domains, health coaching and market analysis. The work has a careful accounting for ethical issues when dealing with personal data.
The other nominated Best Paper candidate that received an honorable mention is:
Human-in-the-Loop Learning for Personalized Diet Monitoring from Unstructured Mobile Data by Niloofar Hezarjaribi (Washington State Univ), Sepideh Mazrouee (UC San Diego), Saied Hemati (Univ. of Idaho), Naomi Chaytor, Martine Perrigue, and Hassan Ghasemzadeh (Washington State Univ.)
Awarded Article for 2018
The Best Paper Award Committee members for 2018 were Henry Lieberman (chair), Shixia Liu, and Catherine Pelachaud.
The award went to Alan Wagner (Penn State University), Paul Robinette (MIT) and Ayanna Howard (Georgia Tech) for their article Modeling the Human-Robot Trust Phenomenon: A Conceptual Framework based on Risk, which was published in issue 8(4) of TiiS.
The committee’s decision to single out this article was explained as follows:
There is no more important topic than the issue of human trust in technology, and this paper presents a framework for thinking about the tradeoffs involved. Rather than assuming that technology is inherently trustworthy and the only problem is user acceptance; or taking a pessimistic view that focuses on potential problems and failures, this paper provides an overarching framework based on the notion of risk. It backs it up with some experimental results, and does not shy away from presenting both supporting and disconfirming results. It is a good example of the "binocular" point of view that considers both technological and social considerations simultaneously. The results are generalizable to any kind of AI technology, not just robots. It is well-written and easy to read.
The other two nominated Best Paper candidates that received an honorable mention are:
Chronodes: Interactive Multifocus Exploration of Event Sequences by Peter Polack, Shang-Tse Chen, Minsuk Kahng, Kaya De Barbaro, Rahul Basole, Duen Horng Chau (Georgia Tech), and Moushumi Sharmin (Western Washington Univ.)
Crowdsourcing Ground Truth for Medical Relation Extraction by Anca Dumitrache, Lora Aroyo (Virje Univ), and Chris Welty (Google).
Awarded Article for 2017
The Best Paper Award Committee members for 2017 were Shimei Pan (chair), Wai-Tat Fu, and Bamshad Mobasher.
The award went to Marius Kaminskas and Derek Bridge (University College Cork, Ireland) for their article Diversity, Serendipity, Novelty, and Coverage: A Survey and Empirical Analysis of Beyond-Accuracy Objectives in Recommender Systems, which was published in issue 7(1) of TiiS.
The committee’s decision to single out this article was explained as follows: The paper provides an extensive, thorough and well-structured literature review on beyond-accuracy quality measures such as diversity, serendipity, coverage and novelty for recommender systems. To gain insight into the relationship between these measures, a set of experiments were conducted to compare and analyze the impact of different optimizing strategies on these measures. Since beyond-accuracy quality measure is a timely topic in recommender systems research, we believe this work will not only serve as a reference point to diverse beyond-accuracy quality measures but also shed new light on the general topic of recommender systems evaluation, which would have significant impact on the design of future recommender systems in diverse context.
The other two nominated Best Paper candidates were:
Detecting Users’ Cognitive Load by Galvanic Skin Response with Affective Interference Nargess Nourbakhsh (University of Sydney and NICTA, Australia) , Fang Chen and Yang Wang (NICTA, Australia), and Rafael A. Calvo (University of Sydney, Australia)
Have You Lost the Thread? Discovering Ongoing Conversations in Scattered Dialog Blocks by Fabio Massimo Zanzotto and Lorenzo Ferrone (University of Roma Tor Vergata, Italy)
Awarded Article for 2016
The Best Paper Award Committee for 2016 comprised: Henry Lieberman (chair), Joe LaViola, and Remco Chang.
The award went to Weike Pan, Qiang Yang, Yuchao Duan, and Zhong Ming for their article
Transfer Learning for Semi-Supervised Collaborative Recommendation, which was published in issue 6(2) of TiiS.
The committee’s decision to single out this article was explained as follows: The article contributes an innovative approach to recommender systems. Its main contribution is an extension of two existing methods on integrating heterogeneous data (user feedback) in a collaborative recommendation system. Using transfer learning, the authors' extension addresses the issue of uncertainty. This process is iterative: (1) maps learned recommendation (built using labeled data) to unlabeled data, and (2) identifies items that are likely going to be highly reviewed to get further input. Because recommender systems are an important topic in HCI, and this paper introduces a sensible and broadly applicable technique to a wide class of interactive machine learning systems, it makes an important contribution to the field of intelligent interactive systems.
Awarded Article for 2015
The Best Paper Award Committee for 2015 comprised: Matthew Turk (chair), Catherine Pelachaud, and Feng Tian.
The award was granted to Axel J. Soto, Vlado Kešelj, and Evangelos Milios (Dalhousie University, Canada) and Ryan Kiros (University of Toronto, Canada) for their article
Exploratory Visual Analysis and Interactive Pattern Extraction From Semi-Structured Data, which was published in issue 5(3) of TiiS.
The committee’s decision to single out this article was explained as follows: This research presents a visual text analytics tool for semi-structured documents, integrating automatic analysis techniques with a novel interactive visual interface to allow pattern-directed exploration of semi-structured text document datasets. The paper makes contributions in both of the key contributing areas of TiiS, machine intelligence and interaction. The work is innovative, thorough, and well presented, with potential for significant real-world impact.
Awarded Article for 2014
The previous (and first) winners of the award were Ben Steichen, Cristina Conati, and Giuseppe Carenini of the University of British Columbia for their article Inferring Visualization Task Properties, User Performance, and User Cognitive Abilities From Eye Gaze Data, which was published in issue 4(2) of TiiS.
Award Criteria
The following four criteria are to be applied:
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Technical excellence
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Innovation
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Potential for impact on the research community and perhaps beyond
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Exemplification of the binocular view of interactive intelligent systems that TiiS aims to promote
Nomination Procedure
An article can be nominated for the award by its reviewers, by the responsible associate editor(s), and by other members of the research community.
All nominators will be required to provide a brief statement in support of their nomination.
Nominators will be required to identify themselves. Self-nominations will be permitted, but they will carry less weight. The award committee will have the discretion to consider any TiiS article published in the relevant volume even if it received no nomination from outside of the committee.
Conflicts of Interest
An article (co)authored by a current or recent editor-in-chief or by a member of the award committee will not be eligible for the award.
Nomination Form
All nominations, including those by reviewers and associate editors, are to be made via the following form: