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

Exploring the Effects of Natural Language Justifications in Food Recommender Systems 探索自然语言证明在食物推荐系统中的作用

Proceedings of the 29th ACM Conference on User Modeling, Adaptation and Personalization Pub Date : 2021-06-21 DOI: 10.1145/3450613.3456827

C. Musto, A. Starke, C. Trattner, A. Rapp, G. Semeraro

{"title":"Exploring the Effects of Natural Language Justifications in Food Recommender Systems","authors":"C. Musto, A. Starke, C. Trattner, A. Rapp, G. Semeraro","doi":"10.1145/3450613.3456827","DOIUrl":"https://doi.org/10.1145/3450613.3456827","url":null,"abstract":"Users of food recommender systems typically prefer popular recipes, which tend to be unhealthy. To encourage users to select healthier recommendations by making more informed food decisions, we introduce a methodology to generate and present a natural language justification that emphasizes the nutritional content, or health risks and benefits of recommended recipes. We designed a framework that takes a user and two food recommendations as input and produces an automatically generated natural language justification as output, which is based on the user’s characteristics and the recipes’ features. In doing so, we implemented and evaluated eight different justification strategies through two different justification styles (e.g., comparing each recipe’s food features) in an online user study (N = 503). We compared user food choices for two personalized recommendation approaches, popularity-based vs our health-aware algorithm, and evaluated the impact of presenting natural language justifications. We showed that comparative justifications styles are effective in supporting choices for our healthy-aware recommendations, confirming the impact of our methodology on food choices.","PeriodicalId":435674,"journal":{"name":"Proceedings of the 29th ACM Conference on User Modeling, Adaptation and Personalization","volume":"122 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":"122043390","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: 18

Right Way, Right Time: Towards a Better Comprehension of Young Students’ Needs when Looking for Relevant Search Results 正确的方式，正确的时间:在寻找相关搜索结果时更好地理解年轻学生的需求

Proceedings of the 29th ACM Conference on User Modeling, Adaptation and Personalization Pub Date : 2021-06-21 DOI: 10.1145/3450613.3456843

M. Landoni, Mohammad Aliannejadi, T. Huibers, Emiliana Murgia, M. S. Pera

引用次数: 6

Personalisation in Cyber-Physical-Social Systems: A Multi-stakeholder aware Recommendation and Guidance 网络-物理-社会系统中的个性化:一个多利益相关者意识的建议和指导

Proceedings of the 29th ACM Conference on User Modeling, Adaptation and Personalization Pub Date : 2021-06-21 DOI: 10.1145/3450613.3456847

B. Yilma, Y. Naudet, H. Panetto

引用次数: 2

Intelligent Shifting Cues: Increasing the Awareness of Multi-Device Interaction Opportunities 智能移动提示:增加对多设备交互机会的认识

Proceedings of the 29th ACM Conference on User Modeling, Adaptation and Personalization Pub Date : 2021-06-21 DOI: 10.1145/3450613.3456839

Ngoc Thi Nguyen, Agustin Zuniga, Huber Flores, Hyowon Lee, S. Perrault, P. Nurmi

{"title":"Intelligent Shifting Cues: Increasing the Awareness of Multi-Device Interaction Opportunities","authors":"Ngoc Thi Nguyen, Agustin Zuniga, Huber Flores, Hyowon Lee, S. Perrault, P. Nurmi","doi":"10.1145/3450613.3456839","DOIUrl":"https://doi.org/10.1145/3450613.3456839","url":null,"abstract":"The ever-increasing ubiquity of smart devices is creating new opportunities for people to interact and engage with digital information using multiple devices. In the simplest case this can refer to choosing which device to use for a particular task (e.g., phone, laptop or smartwatch), whereas a more complex example is simultaneously taking advantage of the capabilities of different devices (e.g., laptop and smart TV). Despite these types of opportunities becoming increasing available, currently the full potential of multi-device interactions is not being realized as people struggle to take advantage of them. As our first contribution, we study people’s willingness to engage with multi-device interactions and rank the factors that mediate this response through an online survey (N = 60). Our results show that users are strongly in favour of using multiple devices, but lack the awareness or information to engage with them, or feel that establishing the interactions is too laborious and would disrupt the fluidity of the interactions. Motivated by this result, as our second contribution we design and evaluate intelligent shifting cues, visualizations that offer information about available interaction opportunities and how to establish them, and study how they influence users willingness to engage in multi-device interactions. Results of our study show that the cues can be effective in helping people to engage with multiple devices, but that the suitability of the proposed device and fit with task are important mediating factors. We end the paper by deriving design implications for intelligent systems that can support people in engaging with multi-device interactions.","PeriodicalId":435674,"journal":{"name":"Proceedings of the 29th ACM Conference on User Modeling, Adaptation and Personalization","volume":"12 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":"125715379","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: 2

Culture and Health Belief Model: Exploring the Determinants of Physical Activity Among Saudi Adults and the Moderating Effects of Age and Gender 文化与健康信念模型:探索沙特成年人身体活动的决定因素以及年龄和性别的调节作用

Proceedings of the 29th ACM Conference on User Modeling, Adaptation and Personalization Pub Date : 2021-06-21 DOI: 10.1145/3450613.3456826

Najla Almutari, Rita Orji

{"title":"Culture and Health Belief Model: Exploring the Determinants of Physical Activity Among Saudi Adults and the Moderating Effects of Age and Gender","authors":"Najla Almutari, Rita Orji","doi":"10.1145/3450613.3456826","DOIUrl":"https://doi.org/10.1145/3450613.3456826","url":null,"abstract":"Physical inactivity is a significant risk factor for many non-communicable diseases such as heart disease, diabetes, and evidence shows that physical inactivity is one of the highest risk factors for death globally. Research has shown that theory-driven persuasive interventions are more effective at promoting behaviour change than generic ones. However, research on the determinants of physical activity and the moderating effect of age and gender among non-Western culture is limited. To close this gap, we conduct a large-scale study of the determinants of physical activity among 217 participants from Saudi Arabia using the extended Health Belief Model (HBM), a commonly applied behavioural model in health interventions design. We also assessed for the moderating effect of age and gender. Our findings show that Social influence, Cue to action and Perceived severity are the strongest determinants of physical activity in Saudi adults. We map these determinants to their corresponding persuasive strategies that can be used in operationalizing them in persuasive applications for promoting physical activity. Finally, we discuss the implication of our findings and offer design guidelines for persuasive interventions that appeal to both a broad audience and tailored to a particular group depending on their gender and age group.","PeriodicalId":435674,"journal":{"name":"Proceedings of the 29th ACM Conference on User Modeling, Adaptation and Personalization","volume":"45 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":"134281246","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

ACM UMAP 2021 Keynote Addresses ACM UMAP 2021主题演讲

Proceedings of the 29th ACM Conference on User Modeling, Adaptation and Personalization Pub Date : 2021-06-21 DOI: 10.1145/3450613.3465270

J. Masthoff, E. Herder, H. Nissenbaum, M. de Rijke, Julita Vassileva

引用次数: 0

Assessing Cognitive Test Performance Using Automatic Digital Pen Features Analysis 使用自动数字笔特征分析评估认知测试表现

Proceedings of the 29th ACM Conference on User Modeling, Adaptation and Personalization Pub Date : 2021-06-21 DOI: 10.1145/3450613.3456812

Alexander Prange, Daniel Sonntag

{"title":"Assessing Cognitive Test Performance Using Automatic Digital Pen Features Analysis","authors":"Alexander Prange, Daniel Sonntag","doi":"10.1145/3450613.3456812","DOIUrl":"https://doi.org/10.1145/3450613.3456812","url":null,"abstract":"Most cognitive assessments, for dementia screening for example, are conducted with a pen on normal paper. We record these tests with a digital pen as part of a new interactive cognitive assessment tool with automatic analysis of pen input. The clinician can, first, observe the sketching process in real-time on a mobile tablet, e.g., in telemedicine settings or to follow Covid-19 distancing regulations. Second, the results of an automatic test analysis are presented to the clinician in real-time, thereby reducing manual scoring effort and producing objective reports. The presented research describes the architecture of our cognitive assessment tool and examines how accurately different machine learning (ML) models can automatically score cognitive tests, without a semantic content analysis. Our system uses a set of more than 170 pen features, calculated directly from the raw digital pen signal. We evaluate our system with 40 subjects from a geriatrics daycare clinic. Using standard ML techniques our feature set outperforms previous approaches on the cognitive tests we consider, i.e., the Clock Drawing, the Rey-Osterrieth Complex Figure, and the Trail Making Test, by automatically scoring tests with up to 82% accuracy in a binary classification task.","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":"121809067","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

Bayesian Preference Elicitation with Keyphrase-Item Coembeddings for Interactive Recommendation 基于关键词-项目共嵌入的交互推荐贝叶斯偏好激发

Proceedings of the 29th ACM Conference on User Modeling, Adaptation and Personalization Pub Date : 2021-06-21 DOI: 10.1145/3450613.3456814

Hojin Yang, S. Sanner, Ga Wu, J. Zhou

{"title":"Bayesian Preference Elicitation with Keyphrase-Item Coembeddings for Interactive Recommendation","authors":"Hojin Yang, S. Sanner, Ga Wu, J. Zhou","doi":"10.1145/3450613.3456814","DOIUrl":"https://doi.org/10.1145/3450613.3456814","url":null,"abstract":"Interactive (a.k.a. conversational) recommendation systems provide the potential capability to personalize interactions with increasingly prevalent dialog-based AI assistants. In the conversational recommendation setting, a user often has long-term preferences inferred from previous interactions along with ephemeral session-based preferences that need to be efficiently elicited through minimal interaction. Historically, Bayesian preference elicitation methods have proved effective for (i) leveraging prior information to incrementally estimate uncertainty in user preferences as new information is observed, and for (ii) supporting active elicitation of preference feedback to quickly zero in on the best recommendations in a session. Previous work typically focused on eliciting preferences in the space of items or a small set of attributes; in the dialog-based setting, however, we are faced with the task of eliciting preferences in the space of natural language while using this feedback to determine a user’s preferences in item space. To address this task in the era of modern, latent embedding-based recommender systems, we propose a method for coembedding user-item preferences with keyphrase descriptions (i.e., not explicitly known attributes, but rather subjective judgments mined from user reviews or tags) along with a closed-form Bayesian methodology for incrementally estimating uncertainty in user preferences based on elicited keyphrase feedback. We then combine this framework with well-known preference elicitation techniques that can leverage Bayesian posteriors such as Upper Confidence Bounds, Thompson Sampling, and a variety of other methods. Our empirical evaluation on real-world datasets shows that the proposed query selection strategies effectively update user beliefs, leading to high-quality recommendations with a minimal number of keyphrase queries.","PeriodicalId":435674,"journal":{"name":"Proceedings of the 29th ACM Conference on User Modeling, Adaptation and Personalization","volume":"99 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":"133219481","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

Characterising Student Engagement Modes through Low-Level Activity Patterns 通过低层次活动模式表征学生参与模式

Proceedings of the 29th ACM Conference on User Modeling, Adaptation and Personalization Pub Date : 2021-06-21 DOI: 10.1145/3450613.3456818

A. Gledson, Aitor Apaolaza, Sabine Barthold, Franziska Günther, He Yu, Markel Vigo

引用次数: 3

Factors Influencing Privacy Concern for Explanations of Group Recommendation 影响隐私关注的因素:群体推荐的解释

Proceedings of the 29th ACM Conference on User Modeling, Adaptation and Personalization Pub Date : 2021-06-21 DOI: 10.1145/3450613.3456845

Shabnam Najafian, Amra Delic, M. Tkalcic, N. Tintarev

{"title":"Factors Influencing Privacy Concern for Explanations of Group Recommendation","authors":"Shabnam Najafian, Amra Delic, M. Tkalcic, N. Tintarev","doi":"10.1145/3450613.3456845","DOIUrl":"https://doi.org/10.1145/3450613.3456845","url":null,"abstract":"Explanations can help users to better understand why items have been recommended. Additionally, explanations for group recommender systems need to consider further goals than single-user recommender systems. For example, we need to balance group members’ need for privacy with their need for transparency, since a transparent explanation might pose a privacy hazard. In an online experiment with real groups (n=114 participants: 38 groups of size 3), we seek to understand which factors influence people’s privacy concerns when a single explanation is presented to a group in the tourism domain. In particular, we study the direct effects of three factors on privacy concern: a) group members’ personality (using the ‘Big Five’ personality traits), b) specific preference scenarios (i.e., having minority or majority preferences compared to two other group members), c) the type of relationship they have in the group (i.e., loosely coupled heterogeneous, versus tightly coupled homogeneous). We find that for personality two traits, Extroversion, and Agreeableness, each significantly affects the privacy concern. Moreover, having the minority or majority preferences in the group, as well as the type of relationship people have in the group, have a strong and significant influence on participants’ privacy concern. These results suggest that explanations presented to groups need to be adapted to all three factors (personality, type of relationship, and preference scenario) when considering the privacy concern of users.","PeriodicalId":435674,"journal":{"name":"Proceedings of the 29th ACM Conference on User Modeling, Adaptation and Personalization","volume":"45 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":"131157668","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: 12