Proceedings of the 2016 Conference on User Modeling Adaptation and Personalization最新文献_第5页

Biases in Automated Music Playlist Generation: A Comparison of Next-Track Recommending Techniques 自动音乐播放列表生成中的偏差:下一首曲目推荐技术的比较

Proceedings of the 2016 Conference on User Modeling Adaptation and Personalization Pub Date : 2016-07-13 DOI: 10.1145/2930238.2930283

D. Jannach, Iman Kamehkhosh, Geoffray Bonnin

引用次数: 25

Gender Differences in Facial Expressions of Affect During Learning 学习过程中面部表情的性别差异

Proceedings of the 2016 Conference on User Modeling Adaptation and Personalization Pub Date : 2016-07-13 DOI: 10.1145/2930238.2930257

A. Vail, Joseph F. Grafsgaard, K. Boyer, E. Wiebe, James C. Lester

{"title":"Gender Differences in Facial Expressions of Affect During Learning","authors":"A. Vail, Joseph F. Grafsgaard, K. Boyer, E. Wiebe, James C. Lester","doi":"10.1145/2930238.2930257","DOIUrl":"https://doi.org/10.1145/2930238.2930257","url":null,"abstract":"Affective support is crucial during learning, with recent evidence suggesting it is particularly important for female students. Facial expression is a rich channel for affect detection, but a key open question is how facial displays of affect differ by gender during learning. This paper presents an analysis suggesting that facial expressions for women and men differ systematically during learning. Using facial video automatically tagged with facial action units, we find that despite no differences between genders in incoming knowledge, self-efficacy, or personality profile, women displayed one lower facial action unit significantly more than men, while men displayed brow lowering and lip fidgeting more than women. However, numerous facial actions including brow raising and nose wrinkling were strongly correlated with learning in women, whereas only one facial action unit, eyelid raiser, was associated with learning for men. These results suggest that the entire affect adaptation pipeline, from detection to response, may benefit from gender-specific models in order to support students more effectively.","PeriodicalId":339100,"journal":{"name":"Proceedings of the 2016 Conference on User Modeling Adaptation and Personalization","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116301717","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: 13

Predicting Customer Satisfaction in Customer Support Conversations in Social Media Using Affective Features 利用情感特征预测社交媒体客户支持对话中的客户满意度

Proceedings of the 2016 Conference on User Modeling Adaptation and Personalization Pub Date : 2016-07-13 DOI: 10.1145/2930238.2930285

Jonathan Herzig, Guy Feigenblat, Michal Shmueli-Scheuer, D. Konopnicki, A. Rafaeli

引用次数: 26

Semantics-aware Graph-based Recommender Systems Exploiting Linked Open Data 利用关联开放数据的基于语义的基于图的推荐系统

Proceedings of the 2016 Conference on User Modeling Adaptation and Personalization Pub Date : 2016-07-13 DOI: 10.1145/2930238.2930249

C. Musto, P. Lops, Pierpaolo Basile, M. Degemmis, G. Semeraro

{"title":"Semantics-aware Graph-based Recommender Systems Exploiting Linked Open Data","authors":"C. Musto, P. Lops, Pierpaolo Basile, M. Degemmis, G. Semeraro","doi":"10.1145/2930238.2930249","DOIUrl":"https://doi.org/10.1145/2930238.2930249","url":null,"abstract":"The ever increasing interest in semantic technologies and the availability of several open knowledge sources have fueled recent progress in the field of recommender systems. In this paper we feed recommender systems with features coming from the Linked Open Data (LOD) cloud - a huge amount of machine-readable knowledge encoded as RDF statements - with the aim of improving recommender systems effectiveness. In order to exploit the natural graph-based structure of RDF data, we study the impact of the knowledge coming from the LOD cloud on the overall performance of a graph-based recommendation algorithm. In more detail, we investigate whether the integration of LOD-based features improves the effectiveness of the algorithm and to what extent the choice of different feature selection techniques influences its performance in terms of accuracy and diversity. The experimental evaluation on two state of the art datasets shows a clear correlation between the feature selection technique and the ability of the algorithm to maximize a specific evaluation metric. Moreover, the graph-based algorithm leveraging LOD-based features is able to overcome several state of the art baselines, such as collaborative filtering and matrix factorization, thus confirming the effectiveness of the proposed approach.","PeriodicalId":339100,"journal":{"name":"Proceedings of the 2016 Conference on User Modeling Adaptation and Personalization","volume":"50 4","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120886586","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: 44

Picture-based Approach to Group Recommender Systems in the E-Tourism Domain 基于图片的电子旅游领域团体推荐系统研究

Proceedings of the 2016 Conference on User Modeling Adaptation and Personalization Pub Date : 2016-07-13 DOI: 10.1145/2930238.2930368

Amra Delic

引用次数: 4

Harnessing Crowdsourced Recommendation Preference Data from Casual Gameplay 利用来自休闲玩法的众包推荐偏好数据

Proceedings of the 2016 Conference on User Modeling Adaptation and Personalization Pub Date : 2016-07-13 DOI: 10.1145/2930238.2930260

Barry Smyth, Rachael Rafter, Sam Banks

引用次数: 3

Open Social Learner Models for Self-Regulated Learning and Learning Motivation 自我调节学习和学习动机的开放社会学习者模型

Proceedings of the 2016 Conference on User Modeling Adaptation and Personalization Pub Date : 2016-07-13 DOI: 10.1145/2930238.2930375

Julio Guerra

引用次数: 5

Identifying Grey Sheep Users in Collaborative Filtering: A Distribution-Based Technique 协同过滤中的灰羊用户识别:一种基于分布的技术

Proceedings of the 2016 Conference on User Modeling Adaptation and Personalization Pub Date : 2016-07-13 DOI: 10.1145/2930238.2930242

Benjamin Gras, A. Brun, A. Boyer

引用次数: 22

Adaptive Training Environment without Prior Knowledge: Modeling Feedback Selection as a Multi-armed Bandit Problem 无先验知识的自适应训练环境:多臂强盗问题的反馈选择建模

Proceedings of the 2016 Conference on User Modeling Adaptation and Personalization Pub Date : 2016-07-13 DOI: 10.1145/2930238.2930256

R. Frenoy, Yann Soullard, I. Thouvenin, O. Gapenne

{"title":"Adaptive Training Environment without Prior Knowledge: Modeling Feedback Selection as a Multi-armed Bandit Problem","authors":"R. Frenoy, Yann Soullard, I. Thouvenin, O. Gapenne","doi":"10.1145/2930238.2930256","DOIUrl":"https://doi.org/10.1145/2930238.2930256","url":null,"abstract":"Pedagogical Action Selection (PAS) is a major issue for intelligent tutoring and training systems. Expert knowledge provides useful insights to build strategies that relate students representation to PAS, but it can be difficult to collect. Furthermore, the influence of a specific action may vary across students, which is rarely reflected in expert knowledge. As part of an automatic gesture training system, we propose to model the co-evolution between a student and a training environment in order to provide personalized action selection. The proposed approach is based on three models representing the student, the environment, and the interactions between these two entities. The latter model sees the PAS as a multi-armed bandit problem, each arm representing a possible action. Thus, PAS personalization only relies on the interactions between the student and the learning environment, without any prior knowledge. Two experiments, one in a simulated environment and a second in a calligraphy training environment, highlight the model ability to personalize action selection, and the benefits of this ability on students skill acquisition.","PeriodicalId":339100,"journal":{"name":"Proceedings of the 2016 Conference on User Modeling Adaptation and Personalization","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116017355","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: 7

Automatic Teacher Modeling from Live Classroom Audio 从现场教室音频自动教师建模

Proceedings of the 2016 Conference on User Modeling Adaptation and Personalization Pub Date : 2016-07-13 DOI: 10.1145/2930238.2930250

P. Donnelly, Nathaniel Blanchard, Borhan Samei, A. Olney, Xiaoyi Sun, Brooke Ward, Sean Kelly, M. Nystrand, S. D’Mello

{"title":"Automatic Teacher Modeling from Live Classroom Audio","authors":"P. Donnelly, Nathaniel Blanchard, Borhan Samei, A. Olney, Xiaoyi Sun, Brooke Ward, Sean Kelly, M. Nystrand, S. D’Mello","doi":"10.1145/2930238.2930250","DOIUrl":"https://doi.org/10.1145/2930238.2930250","url":null,"abstract":"We investigate automatic analysis of teachers' instructional strategies from audio recordings collected in live classrooms. We collected a data set of teacher audio and human-coded instructional activities (e.g., lecture, question and answer, group work) in 76 middle school literature, language arts, and civics classes from eleven teachers across six schools. We automatically segment teacher audio to analyze speech vs. rest patterns, generate automatic transcripts of the teachers' speech to extract natural language features, and compute low-level acoustic features. We train supervised machine learning models to identify occurrences of five key instructional segments (Question & Answer, Procedures and Directions, Supervised Seatwork, Small Group Work, and Lecture) that collectively comprise 76% of the data. Models are validated independently of teacher in order to increase generalizability to new teachers from the same sample. We were able to identify the five instructional segments above chance levels with F1 scores ranging from 0.64 to 0.78. We discuss key findings in the context of teacher modeling for formative assessment and professional development.","PeriodicalId":339100,"journal":{"name":"Proceedings of the 2016 Conference on User Modeling Adaptation and Personalization","volume":"86 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131521472","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: 44