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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.
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)?
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.
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.
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.
The following four criteria are to be applied:
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.
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.
All nominations, including those by reviewers and associate editors, are to be made via the following form: