Proceedings of the 29th ACM Conference on User Modeling, Adaptation and Personalization最新文献_第3页

Pivoting Image-based Profiles Toward Privacy: Inhibiting Malicious Profiling with Adversarial Additions 将基于图像的配置文件转向隐私:抑制带有对抗性添加的恶意配置文件

Proceedings of the 29th ACM Conference on User Modeling, Adaptation and Personalization Pub Date : 2021-06-21 DOI: 10.1145/3450613.3456832

Zhuoran Liu, Zhengyu Zhao, M. Larson

{"title":"Pivoting Image-based Profiles Toward Privacy: Inhibiting Malicious Profiling with Adversarial Additions","authors":"Zhuoran Liu, Zhengyu Zhao, M. Larson","doi":"10.1145/3450613.3456832","DOIUrl":"https://doi.org/10.1145/3450613.3456832","url":null,"abstract":"Users build up profiles online consisting of items that they have shared or interacted with. In this work, we look at profiles that consist of images. We address the issue of privacy-sensitive information being automatically inferred from these user profiles, against users’ will and best interest. We introduce the concept of a privacy pivot, which is a strategic change that users can make in their sharing that will inhibit malicious profiling. Importantly, the pivot helps put privacy control into the hands of the users. Further, it does not require users to delete any of the existing images in their profiles, nor does it require a radical change in their sharing intentions, i.e., what they would like to communicate with their profile. Previous work has investigated adversarial images for privacy protection, but has focused on individual images. Here, we move further to study image sets comprising image profiles. We define a conceptual formulation of the challenge of the privacy pivot in the form of an “Anti-Profiling Model”. Within this model, we propose a basic pivot solution that uses adversarial additions to effectively inhibit the predictions of profilers using set-based image classification.","PeriodicalId":435674,"journal":{"name":"Proceedings of the 29th ACM Conference on User Modeling, Adaptation and Personalization","volume":"60 3","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121004584","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

Effects and Ways of Tailored Gamification in Software-Based Training in Cognitive Rehabilitation 定制化游戏化在认知康复软件训练中的效果与方法

Proceedings of the 29th ACM Conference on User Modeling, Adaptation and Personalization Pub Date : 2021-06-21 DOI: 10.1145/3450613.3456828

Mareike Gabele, Juliane Weicker, Sebastian Wagner, Andrea Thoms, Steffi Hußlein, C. Hansen

{"title":"Effects and Ways of Tailored Gamification in Software-Based Training in Cognitive Rehabilitation","authors":"Mareike Gabele, Juliane Weicker, Sebastian Wagner, Andrea Thoms, Steffi Hußlein, C. Hansen","doi":"10.1145/3450613.3456828","DOIUrl":"https://doi.org/10.1145/3450613.3456828","url":null,"abstract":"A high level of motivation and frequent training are relevant in software-based rehabilitation to improve cognitive functioning after acquired brain injury. We evaluated the benefit of tailored user-centered gamification elements in a clinical study with N=83 outpatients undergoing three weeks of cognitive training in their home environment. The use of gamification in relation to the patient’s player type was explored in three steps. First, we determined the individual player types and related requests for specific game elements by means of questionnaires. Afterwards, we examined the effect of gamified training based on a non-player character and training progress within a metaphor. We considered secondly the individual perception and emotional effect and thirdly the performance based on training duration. 37 elements were requested by patients of all types, 18 elements were partially requested, and 4 elements were rejected. A comparison shows that the requested game elements partly differ between healthy persons and patients. Overall, gamification was perceived positively and gamified training leads to an increase in enjoyment compared to non-gamified training. In detail, however, there were different effects on the individual player types: socialisers experienced more enjoyment while achievers perceived higher competence throughout gamified cognitive training. Also, differences in performance in training duration were found. Within gamified training, socialisers trained significantly more than patients not primarily assigned to this type. In contrast, no significant difference was found for achievers. By showing modulating requests and effects in player types, our results support user-centered tailoring of game elements in the development of software-based cognitive training in rehabilitation.","PeriodicalId":435674,"journal":{"name":"Proceedings of the 29th ACM Conference on User Modeling, Adaptation and Personalization","volume":"18 1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124072025","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

Second Chance for a First Impression? Trust Development in Intelligent System Interaction 给第一印象的第二次机会?智能系统交互中的信任发展

Proceedings of the 29th ACM Conference on User Modeling, Adaptation and Personalization Pub Date : 2021-06-21 DOI: 10.1145/3450613.3456817

Suzanne Tolmeijer, U. Gadiraju, Ramya Ghantasala, Akshit Gupta, Abraham Bernstein

{"title":"Second Chance for a First Impression? Trust Development in Intelligent System Interaction","authors":"Suzanne Tolmeijer, U. Gadiraju, Ramya Ghantasala, Akshit Gupta, Abraham Bernstein","doi":"10.1145/3450613.3456817","DOIUrl":"https://doi.org/10.1145/3450613.3456817","url":null,"abstract":"There is a growing use of intelligent systems to support human decision-making across several domains. Trust in intelligent systems, however, is pivotal in shaping their widespread adoption. Little is currently understood about how trust in an intelligent system evolves over time and how it is mediated by the accuracy of the system. We aim to address this knowledge gap by exploring trust formation over time and its relation to system accuracy. To that end, we built an intelligent house recommendation system and carried out a longitudinal study consisting of 201 participants across 3 sessions in a week. In each session, participants were tasked with finding housing that fit a given set of constraints using a conventional web interface that reflected a typical housing search website. Participants could choose to use an intelligent decision support system to help them find the right house. Depending on the group, participants received a variation of accurate or inaccurate advice from the intelligent system throughout each session. We measured trust using a trust in automation scale at the end of each session. We found evidence suggesting that trust development is a slow process that evolves over multiple sessions, and that first impressions of the intelligent system are highly influential. Our results echo earlier research on trust formation in single session interactions, corroborating that reliability, validity, predictability, and dependability all influence trust formation. We also found that the age of the participants and their affinity with technology had an effect on their trust in the intelligent system. Our findings highlight the importance of first impressions and improvement of system accuracy for trust development. Hence, our study is an important first step in understanding trust development, breakdown of trust, and trust repair over multiple system interactions, informing improved system design.","PeriodicalId":435674,"journal":{"name":"Proceedings of the 29th ACM Conference on User Modeling, Adaptation and Personalization","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122096435","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: 25

User-Centric Item Characteristics for Modeling Users and Improving Recommendations 以用户为中心的项目特征:用户建模和改进推荐

Proceedings of the 29th ACM Conference on User Modeling, Adaptation and Personalization Pub Date : 2021-06-21 DOI: 10.1145/3450613.3459659

Elham Motamedi

{"title":"User-Centric Item Characteristics for Modeling Users and Improving Recommendations","authors":"Elham Motamedi","doi":"10.1145/3450613.3459659","DOIUrl":"https://doi.org/10.1145/3450613.3459659","url":null,"abstract":"The PhD research presented in this paper discusses the idea of using user-centric item characteristics (UCIC) such as the eudaimonic/hedonic quality of multimedia items for achieving a better performance of the recommender systems. UCIC is the characteristics of the item that has its root in the way users perceive the item. Different users have different perceptions and therefore UCIC is a distribution of these perceptions of the item characteristic. For example, movies have a different value of UCIC concerning the induced emotion in users. Therefore, the quality of induced emotion is the characteristic of the item that its value is changed based on how the item is triggering different emotional reactions in users. One of our objectives is to predict the value of UCIC for the items. One possibility of describing UCIC can be by predicting mean and standard variation of perception of users of the specific item characteristic. Another objective is to use the variance in perception of the item characteristic in different users to personalize the recommendations based on the predicted score of the perceived item characteristic for different users. The thesis is composed of three original scientific contributions: (i) devise a method for labeling items with UCIC value and in particular the UCIC of hedonic/eudaimonic quality, (ii) devise models of user behavior based on the perception of item characteristic and in particular eudaimonic/hedonic perception, (iii) devise recommender systems with the incorporation of UCIC concepts for generating more accurate recommendations.","PeriodicalId":435674,"journal":{"name":"Proceedings of the 29th ACM Conference on User Modeling, Adaptation and Personalization","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121303934","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

Multi-Method Evaluation of Adaptive Systems 自适应系统的多方法评价

Proceedings of the 29th ACM Conference on User Modeling, Adaptation and Personalization Pub Date : 2021-06-21 DOI: 10.1145/3450613.3457122

Christine Bauer

引用次数: 0

Uncovering Personal and Context-Dependent Display Preferences in Mobile Newsreader App 在移动新闻阅读器应用程序中揭示个人和上下文相关的显示偏好

Proceedings of the 29th ACM Conference on User Modeling, Adaptation and Personalization Pub Date : 2021-06-21 DOI: 10.1145/3450613.3456808

Emir Hasanbegovic, V. Pejović

引用次数: 0

Model-Agnostic Counterfactual Explanations of Recommendations 模型不可知的建议反事实解释

Proceedings of the 29th ACM Conference on User Modeling, Adaptation and Personalization Pub Date : 2021-06-21 DOI: 10.1145/3450613.3456846

Vassilis Kaffes, Dimitris Sacharidis, G. Giannopoulos

{"title":"Model-Agnostic Counterfactual Explanations of Recommendations","authors":"Vassilis Kaffes, Dimitris Sacharidis, G. Giannopoulos","doi":"10.1145/3450613.3456846","DOIUrl":"https://doi.org/10.1145/3450613.3456846","url":null,"abstract":"Explanations for algorithmically generated recommendations is an important requirement for transparent and trustworthy recommender systems. When the internal recommendation model is not inherently interpretable (e.g., most contemporary systems are complex and opaque), or when access to the system is not available (e.g., recommendation as a service), explanations have to be generated post-hoc, i.e., after the system is trained. In this common setting, the standard approach is to provide plausible interpretations of the observed outputs of the system, e.g., by building a simple surrogate model that is inherently interpretable, and explaining that model. This however has several drawbacks. First, such explanations are not truthful, as they are rationalizations of the observed inputs and outputs constructed by another system. Second, there are privacy concerns, as to train a surrogate model, one has to know the interactions from users other than the one who seeks an explanation. Third, such explanations may not be scrutable and actionable, as they typically return weights for items or other users that are difficult to comprehend, and hard to act upon so to improve the quality of one’s recommendations. In this work, we present a model-agnostic explanation mechanism that is truthful, private, scrutable, and actionable. The key idea is to provide counterfactual explanations, defined as those small changes to the user’s interaction history that are responsible for observing the recommendation output to be explained. Without access to the internal recommendation model, finding concise counterfactual explanations is a hard search problem. We propose several strategies that seek to efficiently extract concise explanations under constraints. Experimentally, we show that these strategies are more efficient and effective than exhaustive and random search.","PeriodicalId":435674,"journal":{"name":"Proceedings of the 29th ACM Conference on User Modeling, Adaptation and Personalization","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129677641","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: 14

Data-Driven Modeling of Learners’ Individual Differences for Predicting Engagement and Success in Online Learning 学习者个体差异的数据驱动模型用于预测在线学习的投入和成功

Proceedings of the 29th ACM Conference on User Modeling, Adaptation and Personalization Pub Date : 2021-06-21 DOI: 10.1145/3450613.3456834

Kamil Akhuseyinoglu, Peter Brusilovsky

{"title":"Data-Driven Modeling of Learners’ Individual Differences for Predicting Engagement and Success in Online Learning","authors":"Kamil Akhuseyinoglu, Peter Brusilovsky","doi":"10.1145/3450613.3456834","DOIUrl":"https://doi.org/10.1145/3450613.3456834","url":null,"abstract":"Individual differences have been recognized as an important factor in the learning process. However, there are few successes in using known dimensions of individual differences in solving an important problem of predicting student performance and engagement in online learning. At the same time, learning analytics research has demonstrated that the large volume of learning data collected by modern e-learning systems could be used to recognize student behavior patterns and could be used to connect these patterns with measures of student performance. Our paper attempts to bridge these two research directions. By applying a sequence mining approach to a large volume of learner data collected by an online learning system, we build models of student learning behavior. However, instead of following modern work on behavior mining (i.e., using this behavior directly for performance prediction tasks), we attempt to follow traditional work on modeling individual differences in quantifying this behavior on a latent data-driven personality scale. Our research shows that this data-driven model of individual differences performs significantly better than several traditional models of individual differences in predicting important parameters of the learning process, such as success and engagement.","PeriodicalId":435674,"journal":{"name":"Proceedings of the 29th ACM Conference on User Modeling, Adaptation and Personalization","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124120802","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: 8

Personalized Persuasive Technologies for Engagement and Behaviour Change 参与和行为改变的个性化说服技术

Proceedings of the 29th ACM Conference on User Modeling, Adaptation and Personalization Pub Date : 2021-06-21 DOI: 10.1145/3450613.3465414

Julita Vassileva

{"title":"Personalized Persuasive Technologies for Engagement and Behaviour Change","authors":"Julita Vassileva","doi":"10.1145/3450613.3465414","DOIUrl":"https://doi.org/10.1145/3450613.3465414","url":null,"abstract":"1 SUMMARY The Covid-19 pandemic has urgently forced public attention on the impact of individual behaviours on the community’s safety, health, and prosperity. Health authorities, politicians, and the media appeal to citizens to engage in safe behaviours – wearing masks, social distancing, hygiene, vaccinating – for the benefit of the vulnerable, and ultimately, for every member of society. As known in the physics of complex systems and economics, macro-level phenomena result from patterns of micro-level behaviours of individuals. Small changes in individual behaviours, adopted massively due to incentives, constraints (laws and regulations), or powerful ideas, lead to macro-level changes in the economy, society, and the ecological or epidemiological situation that can benefit or harm everyone. Changing human behaviour has been the explicit purpose of every society’s educational, legal, and political system in history. The development of digital technologies has enabled the promotion and support of behaviour change at a personal level through personalized education and persuasion, rewards and incentives, monitoring, tracking, and policing human behaviours. Persuasive technologies have been developed with a wide spectrum of purposes. On one side of the spectrum, one can find technologies that benefit mostly the individual (e.g. recommender systems, intelligent tutoring systems). In the middle of the spectrum are technologies","PeriodicalId":435674,"journal":{"name":"Proceedings of the 29th ACM Conference on User Modeling, Adaptation and Personalization","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133009882","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

Translating a Typing-Based Adaptive Learning Model to Speech-Based L2 Vocabulary Learning 基于类型的自适应学习模式到基于语音的二语词汇学习

Proceedings of the 29th ACM Conference on User Modeling, Adaptation and Personalization Pub Date : 2021-06-21 DOI: 10.1145/3450613.3456825

T. Wilschut, Maarten van der Velde, Florian Sense, Z. Fountas, H. van Rijn

{"title":"Translating a Typing-Based Adaptive Learning Model to Speech-Based L2 Vocabulary Learning","authors":"T. Wilschut, Maarten van der Velde, Florian Sense, Z. Fountas, H. van Rijn","doi":"10.1145/3450613.3456825","DOIUrl":"https://doi.org/10.1145/3450613.3456825","url":null,"abstract":"Memorising vocabulary is an important aspect of formal foreign language learning. Advances in cognitive psychology have led to the development of adaptive learning systems that make vocabulary learning more efficient. These computer-based systems measure learning performance in real time to create optimal study strategies for individual learners. While such adaptive learning systems have been successfully applied to written word learning, they have thus far seen little application in spoken word learning. Here we present a system for adaptive, speech-based word learning. We show that it is possible to improve the efficiency of speech-based learning systems by applying a modified adaptive model that was originally developed for typing-based word learning. This finding contributes to a better understanding of the memory processes involved in speech-based word learning. Furthermore, our work provides a basis for the development of language learning applications that use real-time pronunciation assessment software to score the accuracy of the learner’s pronunciations. Speech-based learning applications are educationally relevant because they focus on what may be the most important aspect of language learning: to practice speech.","PeriodicalId":435674,"journal":{"name":"Proceedings of the 29th ACM Conference on User Modeling, Adaptation and Personalization","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132606890","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: 3