Proceedings of the 31st ACM Conference on User Modeling, Adaptation and Personalization最新文献_第4页

Curb Your Procrastination: A Study of Academic Procrastination Behaviors vs. A Planning and Time Management App 抑制你的拖延:学习拖延行为与计划和时间管理应用程序的研究

Proceedings of the 31st ACM Conference on User Modeling, Adaptation and Personalization Pub Date : 2023-06-18 DOI: 10.1145/3565472.3592953

Siqian Zhao, Shaghayegh Sherry Sahebi, Reza Feyzi Behnagh

{"title":"Curb Your Procrastination: A Study of Academic Procrastination Behaviors vs. A Planning and Time Management App","authors":"Siqian Zhao, Shaghayegh Sherry Sahebi, Reza Feyzi Behnagh","doi":"10.1145/3565472.3592953","DOIUrl":"https://doi.org/10.1145/3565472.3592953","url":null,"abstract":"Procrastination is a major issue faced by students which can lead to negative impacts on their academic performance and mental health. Productivity tools aim to help individuals to alleviate this behavior by providing self-regulatory support. However, the processes of how these applications help students conquer academic procrastination are under-explored. Particularly, it is essential to understand what aspects of these applications help which kinds of students in accomplishing their academic tasks. In this paper, we address this gap by presenting an academic planning and time management app (Proccoli) and a study designed to understand the association between student procrastination modeling, in-app behaviors, and perceived performance with app evaluation. As the core of our study, we analyze student perceptions of Proccoli and its impact on their study tasks and time management skills. Then, we model student procrastination behaviors by Hawkes process mining, assess student in-app behaviors by specifying planning and performance-related measures and evaluate the relationship between student behaviors and the evaluation survey results. Our study shows a need for personalized self-regulation support in Proccoli, as students with different in-app studying behaviors are found to have different perceptions of the app functionalities and the association between the prompts for social accountability students received by using Proccoli and their procrastination behavior is significant.","PeriodicalId":309249,"journal":{"name":"Proceedings of the 31st ACM Conference on User Modeling, Adaptation and Personalization","volume":"57 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129296716","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}

引用次数: 0

Accountable Knowledge-aware Recommender Systems 负责任的知识意识推荐系统

Proceedings of the 31st ACM Conference on User Modeling, Adaptation and Personalization Pub Date : 2023-06-18 DOI: 10.1145/3565472.3595605

P. Lops, C. Musto, Marco Polignano

{"title":"Accountable Knowledge-aware Recommender Systems","authors":"P. Lops, C. Musto, Marco Polignano","doi":"10.1145/3565472.3595605","DOIUrl":"https://doi.org/10.1145/3565472.3595605","url":null,"abstract":"Knowledge-aware algorithms represent one of the most innovative research directions in the area of recommender systems. The use of different types of content representation requires new methods to extract descriptive features to adopt in the recommendation process. The literature on knowledge-aware recommender systems is actually rich and constantly evolving in terms of both techniques and software libraries to implement them. This makes also difficult to define reproducible recommendation pipelines, making the accountability of recommender systems a challenge. This tutorial aims to discuss the most recent trends in the area of knowledge-aware recommender systems, including novel representation methods for textual content, and discuss how to implement reproducible pipelines for knowledge-aware recommender systems. We pursue our goals by using a comprehensive Python framework called ClayRS1 to deal with knowledge-aware recommender systems. We would like to provide: (i) common ground for researchers and practitioners interested in the latest knowledge-aware techniques for user modeling and recommender systems; (ii) a practical way for implementing the whole recommendation pipeline, ranging from the content processing for text to the generation of recommendations and the evaluation of their performance.","PeriodicalId":309249,"journal":{"name":"Proceedings of the 31st ACM Conference on User Modeling, Adaptation and Personalization","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133506473","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}

引用次数: 1

User Models as Digital Twins: Using Webassembly Techniques to ensure Privacy, Transparency and Control in Personalization 作为数字双胞胎的用户模型:使用Webassembly技术确保个性化中的隐私、透明度和控制

Proceedings of the 31st ACM Conference on User Modeling, Adaptation and Personalization Pub Date : 2023-06-18 DOI: 10.1145/3565472.3595613

R. Deters, Julita Vassileva

引用次数: 0

Modelling the Training Practices of Recreational Marathon Runners to Make Personalised Training Recommendations 模拟休闲马拉松运动员的训练实践，提出个性化训练建议

Proceedings of the 31st ACM Conference on User Modeling, Adaptation and Personalization Pub Date : 2023-06-18 DOI: 10.1145/3565472.3592952

Ciara Feely, B. Caulfield, A. Lawlor, Barry Smyth

{"title":"Modelling the Training Practices of Recreational Marathon Runners to Make Personalised Training Recommendations","authors":"Ciara Feely, B. Caulfield, A. Lawlor, Barry Smyth","doi":"10.1145/3565472.3592952","DOIUrl":"https://doi.org/10.1145/3565472.3592952","url":null,"abstract":"These days we have all become increasingly aware of the role that exercise plays in a healthy lifestyle. Activities such as cycling, triathlons, and running have become popular ways for people to keep fit and test their abilities. For recreational athletes there is no shortage of training advice or programmes to follow, yet most offer only one-size-fits-all, or minimally tailored guidance, which often leaves novices under-supported on their fitness journeys. In this work, we describe a case-based reasoning system to generate personalised training recommendations for marathon runners, based on their training histories and the training histories of similar runners with comparable race goals. The system harnesses the type of activity data that is routinely collected by smartwatches and apps like Strava. It uses prefactual explanations to suggest to runners how they may wish to adjust their training as their fitness goals evolve. We evaluate the approach using a large-scale dataset of more than 300,000 real-world runners and we show that it is feasible to generate tailored, personalised recommendations for up to 80% of these runners. Additionally, we show that the recommendations produced are realistic and reasonable for a runner to implement, as part of their training programme. These suggestions typically include a small number (3-5) of incremental training adaptations, such as a change in weekly distance, long-run distance, or mean training pace. We argue that by engaging runners in this type of dialog about their training progress and race goals, we can better support novice runners, as their training unfolds, which may help to keep runners motivated on their long journey to race day.","PeriodicalId":309249,"journal":{"name":"Proceedings of the 31st ACM Conference on User Modeling, Adaptation and Personalization","volume":"152 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122045086","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}

引用次数: 0

Toward Changing Users behavior with Emotion-based Adaptive Systems 基于情感的自适应系统改变用户行为

Proceedings of the 31st ACM Conference on User Modeling, Adaptation and Personalization Pub Date : 2023-06-18 DOI: 10.1145/3565472.3595614

M. Alipour, Mahyar Tourchi Moghaddam, Karthik Vaidhyanathan, Mikkel Baun Kjærgaard

{"title":"Toward Changing Users behavior with Emotion-based Adaptive Systems","authors":"M. Alipour, Mahyar Tourchi Moghaddam, Karthik Vaidhyanathan, Mikkel Baun Kjærgaard","doi":"10.1145/3565472.3595614","DOIUrl":"https://doi.org/10.1145/3565472.3595614","url":null,"abstract":"Interactive computer systems’ designers emphasize the importance of considering humans, their emotions, and behaviors as first-class entities. Emotions are integral parts of human nature, and ignoring that can lead the interactive systems to failure, low quality, or discomfort. User interfaces (UIs) are increasingly becoming adaptive to users’ various characteristics, intending to improve users’ satisfaction, performance, and decisions. However, the previous approaches proposed for supervising such adaptations are not effectively adopted in real-life problems. This paper proposes the novel approach to adapting UIs to users’ emotions using Model-Free Reinforcement Learning (MFRL). The approach aims to maximize applying the essential adaptations and minimize the unnecessary ones towards users’ task completion and satisfaction. We chose emergency evacuation training as a suitable evaluation domain since people experience intense emotions in potential danger. We performed experiments with a mobile application we developed that acts as a recommender system in emergency training. By taking contextual input of the users’ basic emotions from face recognition, the application intelligently adapts its UI to quickly lead people to safe areas while arousing target emotions. The research includes literature analysis, surveys, and further adopting an iterative process in implementation and experimentation. The evaluation process confirms the efficiency and effectiveness of the MFRL in iterations, as well as compared to other possible UI adaptation techniques, i.e., rule-based and sequential adaptation.","PeriodicalId":309249,"journal":{"name":"Proceedings of the 31st ACM Conference on User Modeling, Adaptation and Personalization","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131871158","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}

引用次数: 0

Improving Proactive Dialog Agents Using Socially-Aware Reinforcement Learning 使用社会意识强化学习改进主动对话代理

Proceedings of the 31st ACM Conference on User Modeling, Adaptation and Personalization Pub Date : 2022-11-25 DOI: 10.1145/3565472.3595611

Matthias Kraus, Nicolas Wagner, Ron Riekenbrauck, W. Minker

引用次数: 2

Proceedings of the 31st ACM Conference on User Modeling, Adaptation and Personalization 第31届ACM用户建模、适应和个性化会议论文集

Proceedings of the 31st ACM Conference on User Modeling, Adaptation and Personalization Pub Date : 1900-01-01 DOI: 10.1145/3565472

引用次数: 0