@conference{Le2021b,

title = {VXSlate: Combining Head Movement and Mobile Touch for Large Virtual Display Interaction},

author = {Khanh-Duy Le and Tanh Quang Tran and Karol Chlasta and Krzysztof Krejtz and Morten Fjeld and Andreas Kunz},

url = {https://conferences.computer.org/vrpub/pdfs/VRW2021-2ANNoldm4A10Ml9f63uYC9/136700a528/136700a528.pdf

https://www.youtube.com/watch?v=N8ZJlKWj4mk&ab_channel=DuyL%C3%AAKh%C3%A1nh},

doi = { 10.1109/VRW52623.2021.00146},

year  = {2021},

date = {2021-02-12},

pages = {528-529},

publisher = {IEEE The Institute of Electrical and Electronics Engineers, Inc.},

organization = {2021 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW).},

abstract = {Virtual Reality (VR) headsets can open opportunities for users to accomplish complex tasks on large virtual displays, using compact setups. However, interacting with large virtual displays using existing interaction techniques might cause fatigue, especially for precise manipulations, due to the lack of physical surfaces. We designed VXSlate, an interaction technique that uses a large virtual display, as an expansion of a tablet. VXSlate combines a user’s head movements, as tracked by the VR headset, and touch interaction on the tablet. The user’s head movements position both a virtual representation of the tablet and of the user’s hand on the large virtual display. The user’s multi-touch interactions perform finely-tuned content manipulations.},

keywords = {Human computer interaction, Human-centered computing, Interaction techniques, SFI MediaFutures, Virtual Reality, WP4: Media Content Interaction and Accessibility},

pubstate = {published},

tppubtype = {conference}

}

@conference{Balog2020,

title = {Measuring Recommendation Explanation Quality: The Conflicting Goals of Explanations},

author = {Krisztian Balog and Filip Radlinski},

url = {https://dl.acm.org/doi/pdf/10.1145/3397271.3401032},

doi = {https://doi.org/10.1145/3397271.3401032},

year  = {2020},

date = {2020-07-01},

booktitle = {Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR '20)},

pages = {329–338},

address = {New York},

abstract = {Explanations have a large effect on how people respond to recommendations. However, there are many possible intentions a system may have in generating explanations for a given recommendation -from increasing transparency, to enabling a faster decision, to persuading the recipient. As a good explanation for one goal may not be good for others, we address the questions of (1) how to robustly measure if an explanation meets a given goal and (2) how the different goals interact with each other. Specifically, this paper presents a first proposal of how to measure the quality of explanations along seven common goal dimensions catalogued in the literature. We find that the seven goals are not independent, but rather exhibit strong structure. Proposing two novel explanation evaluation designs, we identify challenges in evaluation, and provide more efficient measurement approaches of explanation quality.},

note = {Pre SFI},

keywords = {HCI design and evaluation methods, Human-centered computing, Information Systems, Recommender systems, WP2: User Modeling Personalization and Engagement},

pubstate = {published},

tppubtype = {conference}

}

@conference{Starke2020b,

title = {With a little help from my peers: depicting social norms in a recommender interface to promote energy conservation},

author = {Alain D. Starke and Martijn C. Willemsen and Chris C.P. Snijders},

url = {https://dl.acm.org/doi/10.1145/3377325.3377518},

doi = {10.1145/3377325.3377518},

year  = {2020},

date = {2020-03-17},

number = {March 2020},

pages = {1-11},

abstract = {How can recommender interfaces help users to adopt new behaviors? In the behavioral change literature, nudges and norms are studied to understand how to convince people to take action (e.g. towel re-use is boosted when stating that `75% of hotel guests' do so), but what is advised is typically not personalized. Most recommender systems know what to recommend in a personalized way, but not much research has considered how to present such advice to help users to change their current habits. We examine the value of presenting normative messages (e.g. `75% of users do X') based on actual user data in a personalized energy recommender interface called `Saving Aid'. In a study among 207 smart thermostat owners, we compared three different normative explanations (`Global', `Similar', and `Experienced' norm rates) to a non-social baseline (`kWh savings'). Although none of the norms increased the total number of chosen measures directly, we show evidence that the effect of norms seems to be mediated by the perceived feasibility of the measures. Also, how norms were presented (i.e. specific source, adoption rate) affected which measures were chosen within our Saving Aid interface.},

keywords = {Decision Support System, Human computer interaction, Human-centered computing, Information Systems, User studies},

pubstate = {published},

tppubtype = {conference}

}

@conference{Starke2017,

title = {Effective User Interface Designs to Increase Energy-efficient Behavior in a Rasch-based Energy Recommender System},

author = {Alain D. Starke and Martijn C. Willemsen and Chris C.P. Snijders},

url = {https://dl.acm.org/doi/abs/10.1145/3109859.3109902},

doi = {10.1145/3109859.3109902},

year  = {2017},

date = {2017-08-27},

number = {August 2017},

pages = {1-9},

abstract = {People often struggle to find appropriate energy-saving measures to take in the household. Although recommender studies show that tailoring a system's interaction method to the domain knowledge of the user can increase energy savings, they did not actually tailor the conservation advice itself. We present two large user studies in which we support users to make an energy-efficient behavioral change by presenting tailored energy-saving advice. Both systems use a one-dimensional, ordinal Rasch scale, which orders 79 energy-saving measures on their behavioral difficulty and link this to a user's energy-saving ability for tailored advice. We established that recommending Rasch-based advice can reduce a user's effort, increase system support and, in turn, increase choice satisfaction and lead to the adoption of more energy-saving measures. Moreover, follow-up surveys administered four weeks later point out that tailoring advice on its feasibility can support behavioral change.},

keywords = {Applied computing, Human-centered computing, Information Systems},

pubstate = {published},

tppubtype = {conference}

}