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RecSys'17 Joint Workshop on Interfaces and Human Decision Making for Recommender Systems

Peter Brusilovsky, Marco de Gemmis, Alexander Felfernig, Pasquale Lops, John O'Donovan, Nava Tintarev, Martijn Willemsen
2017 Proceedings of the Eleventh ACM Conference on Recommender Systems - RecSys '17  
This finding has the potential to improve the efficiency of data collection for applications such as Top-N recommender systems; where we are primarily interested in the ranked order of items, rather than  ...  Psychologists have long recognized a number of biases to which many human raters are prone, and which result in disagreement among raters as to the true gold standard rating of any particular object.  ...  Researchers found that many respondents can be categorised as displaying one or more of these Joint Workshop on Interfaces and Human Decision Making for Recommender Systems, August 27, 2017, Como, Italy  ... 
doi:10.1145/3109859.3109961 dblp:conf/recsys/BrusilovskyGFLO17 fatcat:vishcvo5jrdbnnj24ncydrbcfm

Visualization of Explanations in Recommender Systems

Mohammed Z. Al-Taie, Seifedine Kadry
2014 Journal of Advanced Management Science  
The paper ends with conclusions and perspectives for future work.  Index Terms-recommender systems, explanations, information visualization, decision making, human computer interaction  ...  Then, we talk about the relationship between visualization of explanations and other disciplines such as human computer interaction and decision making.  ...  The integration of recommendation technologies with a profound realization of human decision making can improve the quality of recommendation for users and the predictability of decision outcomes [9]  ... 
doi:10.12720/joams.2.2.140-144 fatcat:gdjmtmeapfhxnoxnatbzfy3ftm

Workshop on human decision making in recommender systems

Li Chen, Marco de Gemmis, Alexander Felfernig, Pasquale Lops, Francesco Ricci, Giovanni Semeraro, Martijn C. Willemsen
2013 Proceedings of the 7th ACM conference on Recommender systems - RecSys '13  
A primary function of recommender systems is to help their users to make better choices and decisions.  ...  The overall goal of the workshop is to analyse and discuss novel techniques and approaches for supporting effective and efficient human decision making in different types of recommendation scenarios.  ...  decision making processes in recommender systems -Active approaches to preference elicitation • User interfaces -User interfaces for decision making -Explanations in Recommender Systems • Evaluation -  ... 
doi:10.1145/2507157.2508002 dblp:conf/recsys/ChenGFLRSW13 fatcat:76iq24lqlrcezggr7drvovxx7y

Automation and Accountability in Decision Support System Interface Design

Mary L. Cummings
2006 The Journal of Technology Studies  
I argue that when developing human computer interfaces for decision support systems that have the ability to harm people, the possibility exists that a moral buffer, a form of psychological distancing,  ...  When the human element is introduced into decision support system design, entirely new layers of social and ethical issues emerge but are not always recognized as such.  ...  For interface designs that require significant human cognitive contribution, especially in decision support arenas that directly impact human life such as weapons and medical systems, it is paramount that  ... 
doi:10.21061/jots.v32i1.a.4 fatcat:z5gjiiiipbaqnnxbtxk7qnt46e

Concept Sharing between Human and Interface Agent under Time Criticality [chapter]

Tetsuo Sawaragi, Teruyuki Ogura
2000 Advances in Networked Enterprises  
method for concept induction and a classical decision theory.  ...  In this paper, instead of designing an automation system that is intended to replace a human operator completely with it and to exclude him/her out of the loop, we introduce an idea of an interface agent  ...  In order to grade up an interface agent from an automated agent to a human collaborative agent, we formulate a decision-making activity of an interface agent analogous to a human.  ... 
doi:10.1007/978-0-387-35529-0_25 fatcat:ccldzfakinf35mfbjexftpmlca

Explainable recommendation: when design meets trust calibration

Mohammad Naiseh, Dena Al-Thani, Nan Jiang, Raian Ali
2021 World wide web (Bussum)  
However, these tools are often seen as closed and intransparent for human decision-makers.  ...  We first conducted a think-aloud study with 16 participants aiming to reveal main trust calibration errors concerning explainability in AI-Human collaborative decision-making tools.  ...  Acknowledgements This work is partially funded by iQ HealthTech and Bournemouth University PGR development fund.  ... 
doi:10.1007/s11280-021-00916-0 pmid:34366701 pmcid:PMC8327305 fatcat:7mipj7ejpfbkxoq53oubsxw7ea

Towards Active Learning Based Smart Assistant for Manufacturing [article]

Patrik Zajec, Jože M. Rožanec, Inna Novalija, Blaž Fortuna, Dunja Mladenić, Klemen Kenda
2021 arXiv   pre-print
A general approach for building a smart assistant that guides a user from a forecast generated by a machine learning model through a sequence of decision-making steps is presented.  ...  The system provides means for knowledge acquisition, gathering data from users. We envision active learning can be used to get data labels where labeled data is scarce.  ...  Acknowledgements This work was supported by the Slovenian Research Agency and the European Union's Horizon 2020 program projects FACTLOG under grant agreement H2020-869951 and STAR under grant agreement  ... 
arXiv:2103.16177v1 fatcat:6jimeqcpbzberl2n2qujkoxuoe

Intelligent pairing assistant for air operation centers

Jeremy Ludwig, Eric Geiselman
2012 Proceedings of the 2012 ACM international conference on Intelligent User Interfaces - IUI '12  
Within an Air Operations Center (AOC), planners make crucial decisions to create the air plan for any given day.  ...  IPA is designed as a plug-in for software systems already in use within AOCs.  ...  ACKNOWLEDGMENTS We would like to thank Todd Cloutier, Drew Decker Donald Hulten, Bart Presnell, Beverly Sanford, and Milt Waddell.  ... 
doi:10.1145/2166966.2167008 dblp:conf/iui/LudwigG12 fatcat:43i2hd47bvg2zklh63u7qugimu

Improved explanatory efficacy on human affect and workload through interactive process in artificial intelligence [article]

Byung Hyung Kim, Seunghun Koh, Sejoon Huh, Sungho Jo
2019 arXiv   pre-print
build personalized recommendation systems.  ...  Ensuring the success of an explanatory interface in interacting with users requires a cyclic, symbiotic relationship between human and artificial intelligence.  ...  With the new weights, the system makes new predictions and recommends a new list of movies for the user.  ... 
arXiv:1912.07416v1 fatcat:qttwlxisu5fe3n7tbda7czjdtm

Adaptation in automated user-interface design

Jacob Eisenstein, Angel Puerta
2000 Proceedings of the 5th international conference on Intelligent user interfaces - IUI '00  
Design problems involve issues of stylistic preference and flexible standards of success; human designers often proceed by intuition and are unaware of following any strict rule-based procedures.  ...  Keywords Model-based interface development, machine learning, decision trees, theory refinement, user interface development tools, interface models, theory refinement  ...  Kim, Kjetil Larsen, David Maulsby, Justin Min, Dat Ngoyen, Tunhow Ou, David Selinger, and Chung-Man Tam for their work on the implementation and use of MOBI-D.  ... 
doi:10.1145/325737.325787 dblp:conf/iui/EisensteinP00 fatcat:6rukcrhtvfagnke6ncij57jurm

Are you aware of what you are watching? Role of machine heuristic in online content recommendations [article]

Soyoung Oh, Eunil Park
2022 arXiv   pre-print
In this study, we designed and conducted a web-based experiment where the participants are invoked machine heuristic by experiencing the whole process of machine or human recommendation system.  ...  To make it worse, people would unreservedly accept such content due to their cognitive heuristic, machine heuristic, which is the rule of thumb that machines are more accurate and trustworthy than humans  ...  Particularly for the systems where users are required to make decisions based, at least partially, on machine recommendations.  ... 
arXiv:2203.08373v1 fatcat:j2n47yskszgoxbmzi4obgtc4ca

Creating More Credible and Persuasive Recommender Systems: The Influence of Source Characteristics on Recommender System Evaluations [chapter]

Kyung-Hyan Yoo, Ulrike Gretzel
2010 Recommender Systems Handbook  
Implications for recommender system research and design are discussed.  ...  This chapter reviews the existing literature on source characteristics in the context of human-human, human-computer, and human-recommender system interactions.  ...  In addition, Schafer [112] suggested that merging the preferences interface and the recommendation elicitation interface within a single interface can make the recommender system be seen as more helpful  ... 
doi:10.1007/978-0-387-85820-3_14 fatcat:w5w3ab2rvrcsrja7e77azohemm

Improved Explanatory Efficacy on Human Affect and Workload Through Interactive Process in Artificial Intelligence

Byung Hyung Kim, Seunghun Koh, Sejoon Huh, Sungho Jo, Sunghee Choi
2020 IEEE Access  
build personalized recommendation systems.  ...  Ensuring the success of an explanatory interface in interacting with users requires a cyclic, symbiotic relationship between human and artificial intelligence.  ...  With the new weights, the system makes new predictions and recommends a new list of movies for the user.  ... 
doi:10.1109/access.2020.3032056 fatcat:mx5gbreg2vaglm5oa4lsbj34py


Freddie Chen
2020 Figshare  
Research about partner recommendation system with zodiac as the main determinant  ...  Decision support systems can be driven by humans, computers, or a combination of the two, while decision support systems can be used as tool to support the decision making process, the users see decision  ...  Partner Recommendation System Partner recommendation system can be categorized as a decision support system, where the decision taken is the partner that recommended for the user.  ... 
doi:10.6084/m9.figshare.12110430.v2 fatcat:euvxn5ltune45jspnwjkpkoaim

Proxy tasks and subjective measures can be misleading in evaluating explainable AI systems

Zana Buçinca, Phoebe Lin, Krzysztof Z. Gajos, Elena L. Glassman
2020 Proceedings of the 25th International Conference on Intelligent User Interfaces  
Explainable artificially intelligent (XAI) systems form part of sociotechnical systems, e.g., human+AI teams tasked with making decisions.  ...  Yet, current XAI systems are rarely evaluated by measuring the performance of human+AI teams on actual decision-making tasks.  ...  We would like to thank Tianyi Zhang and Isaac Lage for helpful feedback.  ... 
doi:10.1145/3377325.3377498 dblp:conf/iui/BucincaLGG20 fatcat:wqv4kvncy5dfngfo2peyfsxgs4
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