{"title":"User's Knowledge and Information Needs in Information Retrieval Evaluation","authors":"Dima El Zein, C. Pereira","doi":"10.1145/3503252.3531325","DOIUrl":"https://doi.org/10.1145/3503252.3531325","url":null,"abstract":"","PeriodicalId":93391,"journal":{"name":"UMAP ... proceedings of the ... Conference on User Modeling, Adaptation and Personalization. UMAP (Conference)","volume":"76 1","pages":"170-178"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78283980","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Personalizing Persuasive Principles to Improve Credibility","authors":"F. N. Koranteng","doi":"10.1145/3503252.3534359","DOIUrl":"https://doi.org/10.1145/3503252.3534359","url":null,"abstract":"","PeriodicalId":93391,"journal":{"name":"UMAP ... proceedings of the ... Conference on User Modeling, Adaptation and Personalization. UMAP (Conference)","volume":"69 1","pages":"331-334"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82614396","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Discrimination and Stereotypical Responses to Robots as a Function of Robot Colorization","authors":"Jessica K. Barfield","doi":"10.1145/3450614.3463411","DOIUrl":"https://doi.org/10.1145/3450614.3463411","url":null,"abstract":"","PeriodicalId":93391,"journal":{"name":"UMAP ... proceedings of the ... Conference on User Modeling, Adaptation and Personalization. UMAP (Conference)","volume":"1 1","pages":"109-114"},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75971770","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Systematic Review of Context-Aware Systems that use Item Response Theory in Learning Environments","authors":"Oscar Yair Ortegón Romero, Leandro Krug Wives","doi":"10.1145/3450614.3464482","DOIUrl":"https://doi.org/10.1145/3450614.3464482","url":null,"abstract":"","PeriodicalId":93391,"journal":{"name":"UMAP ... proceedings of the ... Conference on User Modeling, Adaptation and Personalization. UMAP (Conference)","volume":"42 1","pages":"100-104"},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74047609","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Chatbots in the tourism industry: the effects of communication style and brand familiarity on social presence and brand attitude","authors":"C. V. Hooijdonk","doi":"10.1145/3450614.3463599","DOIUrl":"https://doi.org/10.1145/3450614.3463599","url":null,"abstract":"","PeriodicalId":93391,"journal":{"name":"UMAP ... proceedings of the ... Conference on User Modeling, Adaptation and Personalization. UMAP (Conference)","volume":"53 1","pages":"375-381"},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72648146","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Adjunct Publication of the 29th ACM Conference on User Modeling, Adaptation and Personalization, UMAP 2021, Utrecht, The Netherlands, June 21-25, 2021","authors":"","doi":"10.1145/3450614","DOIUrl":"https://doi.org/10.1145/3450614","url":null,"abstract":"","PeriodicalId":93391,"journal":{"name":"UMAP ... proceedings of the ... Conference on User Modeling, Adaptation and Personalization. UMAP (Conference)","volume":"50 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85110795","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In a world where the use of AI is growing and evolving, where will we be in 5 years? 10 years? 20 years? What role will AI play in our society, and how will humans and AI interact? While there will undoubtedly be scenarios where AI systems will be able to outperform humans, there will also continue to be instances where humans will be a critical part of the process. As researchers explore improvements to AI systems, we also need to explore the interplay between humans and AI, and continue to evolve our understanding of how humans and AI systems can work together, effectively harnessing the benefits of both systems [3]. Designing effective interaction between the human and the AI systems is critical for future use of Human-AI systems [1]. Merely building an AI system that blindly sends recommendations to users has been shown in some cases to decrease human performance [2]. Different models can also have differential impact on user's trust of the model, adherence to the recommendation, and can impact bias in decision making tasks. This talk will highlight important directions for Human-AI research.
{"title":"Does My AI Help or Hurt? Exploring Human-AI Complementarity","authors":"K. Quinn","doi":"10.1145/3340631.3395384","DOIUrl":"https://doi.org/10.1145/3340631.3395384","url":null,"abstract":"In a world where the use of AI is growing and evolving, where will we be in 5 years? 10 years? 20 years? What role will AI play in our society, and how will humans and AI interact? While there will undoubtedly be scenarios where AI systems will be able to outperform humans, there will also continue to be instances where humans will be a critical part of the process. As researchers explore improvements to AI systems, we also need to explore the interplay between humans and AI, and continue to evolve our understanding of how humans and AI systems can work together, effectively harnessing the benefits of both systems [3]. Designing effective interaction between the human and the AI systems is critical for future use of Human-AI systems [1]. Merely building an AI system that blindly sends recommendations to users has been shown in some cases to decrease human performance [2]. Different models can also have differential impact on user's trust of the model, adherence to the recommendation, and can impact bias in decision making tasks. This talk will highlight important directions for Human-AI research.","PeriodicalId":93391,"journal":{"name":"UMAP ... proceedings of the ... Conference on User Modeling, Adaptation and Personalization. UMAP (Conference)","volume":"2015 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73305798","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-06-01Epub Date: 2019-06-07DOI: 10.1145/3320435.3320460
Vikas Ashok, Syed Masum Billah, Yevgen Borodin, I V Ramakrishnan
Web browsing has never been easy for blind people, primarily due to the serial press-and-listen interaction mode of screen readers - their "go-to" assistive technology. Even simple navigational browsing actions on a page require a multitude of shortcuts. Auto-suggesting the next browsing action has the potential to assist blind users in swiftly completing various tasks with minimal effort. Extant auto-suggest feature in web pages is limited to filling form fields; in this paper, we generalize it to any web screen-reading browsing action, e.g., navigation, selection, etc. Towards that, we introduce SuggestOmatic, a personalized and scalable unsupervised approach for predicting the most likely next browsing action of the user, and proactively suggesting it to the user so that the user can avoid pressing a lot of shortcuts to complete that action. SuggestOmatic rests on two key ideas. First, it exploits the user's Action History to identify and suggest a small set of browsing actions that will, with high likelihood, contain an action which the user will want to do next, and the chosen action is executed automatically. Second, the Action History is represented as an abstract temporal sequence of operations over semantic web entities called Logical Segments - a collection of related HTML elements, e.g., widgets, search results, menus, forms, etc.; this semantics-based abstract representation of browsing actions in the Action History makes SuggestOmatic scalable across websites, i.e., actions recorded in one website can be used to make suggestions for other similar websites. We also describe an interface that uses an off-the-shelf physical Dial as an input device that enables SuggestOmatic to work with any screen reader. The results of a user study with 12 blind participants indicate that SuggestOmatic can significantly reduce the browsing task times by as much as 29% when compared with a hand-crafted macro-based web automation solution.
{"title":"Auto-Suggesting Browsing Actions for Personalized Web Screen Reading.","authors":"Vikas Ashok, Syed Masum Billah, Yevgen Borodin, I V Ramakrishnan","doi":"10.1145/3320435.3320460","DOIUrl":"10.1145/3320435.3320460","url":null,"abstract":"<p><p>Web browsing has never been easy for blind people, primarily due to the serial press-and-listen interaction mode of screen readers - their \"go-to\" assistive technology. Even simple navigational browsing actions on a page require a multitude of shortcuts. Auto-suggesting the next browsing action has the potential to assist blind users in swiftly completing various tasks with minimal effort. Extant auto-suggest feature in web pages is limited to filling form fields; in this paper, we generalize it to any web screen-reading browsing action, e.g., navigation, selection, etc. Towards that, we introduce <i>SuggestOmatic</i>, a personalized and scalable unsupervised approach for predicting the most likely next browsing action of the user, and proactively suggesting it to the user so that the user can avoid pressing a lot of shortcuts to complete that action. SuggestOmatic rests on two key ideas. First, it exploits the user's Action History to identify and suggest a small set of browsing actions that will, with high likelihood, contain an action which the user will want to do next, and the chosen action is executed automatically. Second, the Action History is represented as an abstract temporal sequence of operations over semantic web entities called Logical Segments - a collection of related HTML elements, e.g., widgets, search results, menus, forms, etc.; this semantics-based abstract representation of browsing actions in the Action History makes SuggestOmatic scalable across websites, i.e., actions recorded in one website can be used to make suggestions for other similar websites. We also describe an interface that uses an off-the-shelf physical Dial as an input device that enables SuggestOmatic to work with any screen reader. The results of a user study with 12 blind participants indicate that SuggestOmatic can significantly reduce the browsing task times by as much as 29% when compared with a hand-crafted macro-based web automation solution.</p>","PeriodicalId":93391,"journal":{"name":"UMAP ... proceedings of the ... Conference on User Modeling, Adaptation and Personalization. UMAP (Conference)","volume":"2019 ","pages":"252-260"},"PeriodicalIF":0.0,"publicationDate":"2019-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8319855/pdf/nihms-1664019.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39266848","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
My PhD project sits in the design space that investigates how smart technology can support dance education. My aim is to design, implement and evaluate a conceptual and technological solution that captures students' movement using wearable devices and help dance teachers and students enhance their awareness and promote reflection regarding dance skills acquisition using automated personalised feedback (charts, tables, text, etc.). I will explore how to acquire movement data that can represent key aspects of social dance learning, and how to use these data to support of students and teachers. For this, I created a mobile app that records students' movement while they are practicing dance exercises and creates a dance learner model. The learner model's features are exposed through the Open Learner Model to students and their teachers in order to support reflection and increase awareness. With the proposed work I expect to generate a deeper understanding of the aspects of the dance learner model which can be used to promote personalization and adaptation, and positively impact dance learning.
{"title":"Smart Technology for Supporting Dance Education","authors":"Augusto Dias Pereira dos Santos","doi":"10.1145/3079628.3079709","DOIUrl":"https://doi.org/10.1145/3079628.3079709","url":null,"abstract":"My PhD project sits in the design space that investigates how smart technology can support dance education. My aim is to design, implement and evaluate a conceptual and technological solution that captures students' movement using wearable devices and help dance teachers and students enhance their awareness and promote reflection regarding dance skills acquisition using automated personalised feedback (charts, tables, text, etc.). I will explore how to acquire movement data that can represent key aspects of social dance learning, and how to use these data to support of students and teachers. For this, I created a mobile app that records students' movement while they are practicing dance exercises and creates a dance learner model. The learner model's features are exposed through the Open Learner Model to students and their teachers in order to support reflection and increase awareness. With the proposed work I expect to generate a deeper understanding of the aspects of the dance learner model which can be used to promote personalization and adaptation, and positively impact dance learning.","PeriodicalId":93391,"journal":{"name":"UMAP ... proceedings of the ... Conference on User Modeling, Adaptation and Personalization. UMAP (Conference)","volume":"21 1","pages":"335-338"},"PeriodicalIF":0.0,"publicationDate":"2017-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91162278","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
ACM UMAP 2017 is the 25th conference on User Modeling, on Adaptive Hypermedia, or on both together (since 2009). The research has actually been going on for more than 25 years as initially there was a conference only every two years. This keynote offers both reflection on the past and outlook into the future, with the burning question: What makes us UMAP? We perform research on modeling users (individuals as well as groups), not just for fun but to use these models for recommendations and for adaptation. That's not unique to us. In recommender systems analyzing user behavior is needed in order to give better and better recommendations, and likewise an area like educational data mining analyzes how learners study in order to best guide them to new learning material or followup courses. With analysis of social networks and website adaptation we step into the same research area that is covered by the hypertext community. If all of this is "us" but "not just us", where is our identity? One key characteristic of User Modeling is our quest to come up with understandable user models, or scrutable as Judy Kay coins them. The same is true for the adaptation: we strive to understand why certain adaptation happens or why a certain recommendation is given. UMAP research is not complete if we cannot understand the chain that leads from user action to (a perhaps much later) system reaction. As we move from expert-driven adaptation towards data-driven adaptation the problem of understanding the user-modeling-to-adaptation process is becoming harder and harder. But we need this understanding to ensure that adaptation continues to adapt in the right way under continuously changing circumstances (both in what we adapt and in the users and context we adapt to). We need the understanding also to prevent continuous adaptation from creating filter bubbles and to avoid creating the illusion that the recommendations will always be "right" because of the "wisdom of the crowd" principle. One key element has always been missing from UMAP, and this keynote will fill that void: we need to practice what we preach. Therefore, the conference proceedings will only contain this abstract, but there will be a real paper to go with this abstract. That paper cannot be printed because it is adaptive. The URL of the keynote paper is http://gale.win.tue.nl/keynote/.
{"title":"After Twenty-Five Years of User Modeling and Adaptation...What Makes us UMAP?","authors":"P. D. Bra","doi":"10.1145/3079628.3079662","DOIUrl":"https://doi.org/10.1145/3079628.3079662","url":null,"abstract":"ACM UMAP 2017 is the 25th conference on User Modeling, on Adaptive Hypermedia, or on both together (since 2009). The research has actually been going on for more than 25 years as initially there was a conference only every two years. This keynote offers both reflection on the past and outlook into the future, with the burning question: What makes us UMAP? We perform research on modeling users (individuals as well as groups), not just for fun but to use these models for recommendations and for adaptation. That's not unique to us. In recommender systems analyzing user behavior is needed in order to give better and better recommendations, and likewise an area like educational data mining analyzes how learners study in order to best guide them to new learning material or followup courses. With analysis of social networks and website adaptation we step into the same research area that is covered by the hypertext community. If all of this is \"us\" but \"not just us\", where is our identity? One key characteristic of User Modeling is our quest to come up with understandable user models, or scrutable as Judy Kay coins them. The same is true for the adaptation: we strive to understand why certain adaptation happens or why a certain recommendation is given. UMAP research is not complete if we cannot understand the chain that leads from user action to (a perhaps much later) system reaction. As we move from expert-driven adaptation towards data-driven adaptation the problem of understanding the user-modeling-to-adaptation process is becoming harder and harder. But we need this understanding to ensure that adaptation continues to adapt in the right way under continuously changing circumstances (both in what we adapt and in the users and context we adapt to). We need the understanding also to prevent continuous adaptation from creating filter bubbles and to avoid creating the illusion that the recommendations will always be \"right\" because of the \"wisdom of the crowd\" principle. One key element has always been missing from UMAP, and this keynote will fill that void: we need to practice what we preach. Therefore, the conference proceedings will only contain this abstract, but there will be a real paper to go with this abstract. That paper cannot be printed because it is adaptive. The URL of the keynote paper is http://gale.win.tue.nl/keynote/.","PeriodicalId":93391,"journal":{"name":"UMAP ... proceedings of the ... Conference on User Modeling, Adaptation and Personalization. UMAP (Conference)","volume":"5 1","pages":"1"},"PeriodicalIF":0.0,"publicationDate":"2017-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74553683","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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