Panagiotis Germanakos, S. Kleanthous, G. Samaras, V. Dimitrova, B. Steichen
It is our great pleasure to welcome you to the 2nd International workshop on Human Aspects in Adaptive and Personalized Interactive Environments (HAAPIE 2017). HAAPIE 2017 (http://haapie.cs.ucy.ac.cy) is a full-day workshop held on 09 July 2017 in conjunction with the 25th ACM Conference on User Modeling, Adaptation and Personalization (UMAP 2017), 09-12 July 2017 in Bratislava, Slovakia. Nowadays, the profound digital transformation has upgraded the role of the computational system into an intelligent multidimensional communication medium that creates new opportunities, competencies, models and processes. HAAPIE embraces the essence of the human-machine co-existence and aims to bring more inclusively the "human-in-the-loop", adequately supporting the rising multi-purpose goals, needs, requirements, activities and interactions of users through new human-centered adaptive and personalized interactive environments, algorithms and systems. It brings together experts, researchers, students and practitioners from different disciplines for sharing ideas and experiences, lessons learned, approaches and results that could substantially contribute to the broader UMAP community. This year we received 14 submissions from all around the world covering a broad range of topics on the workshop's research themes areas. Each paper has been reviewed by up to 3 members of the IPC with expertise in the respective area to ensure the necessary relevance, quality and novelty.
{"title":"UMAP 2017 HAAPIE (Human Aspects in Adaptive and Personalized Interactive Environments) Workshop Chairs' Preface & Organization","authors":"Panagiotis Germanakos, S. Kleanthous, G. Samaras, V. Dimitrova, B. Steichen","doi":"10.1145/3099023.3099050","DOIUrl":"https://doi.org/10.1145/3099023.3099050","url":null,"abstract":"It is our great pleasure to welcome you to the 2nd International workshop on Human Aspects in Adaptive and Personalized Interactive Environments (HAAPIE 2017). HAAPIE 2017 (http://haapie.cs.ucy.ac.cy) is a full-day workshop held on 09 July 2017 in conjunction with the 25th ACM Conference on User Modeling, Adaptation and Personalization (UMAP 2017), 09-12 July 2017 in Bratislava, Slovakia. Nowadays, the profound digital transformation has upgraded the role of the computational system into an intelligent multidimensional communication medium that creates new opportunities, competencies, models and processes. HAAPIE embraces the essence of the human-machine co-existence and aims to bring more inclusively the \"human-in-the-loop\", adequately supporting the rising multi-purpose goals, needs, requirements, activities and interactions of users through new human-centered adaptive and personalized interactive environments, algorithms and systems. It brings together experts, researchers, students and practitioners from different disciplines for sharing ideas and experiences, lessons learned, approaches and results that could substantially contribute to the broader UMAP community. This year we received 14 submissions from all around the world covering a broad range of topics on the workshop's research themes areas. Each paper has been reviewed by up to 3 members of the IPC with expertise in the respective area to ensure the necessary relevance, quality and novelty.","PeriodicalId":219391,"journal":{"name":"Adjunct Publication of the 25th Conference on User Modeling, Adaptation and Personalization","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117183524","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}
This demo paper presents a Web application for recommending travel regions for independent travelers. Users can specify preferences such as budget and preferred activities and receive suggested trips consisting of multiple regions. We explain the main ideas behind the data model and algorithm of our solution, and give an overview on the implementation
{"title":"A Web-based Application for Recommending Travel Regions","authors":"W. Wörndl","doi":"10.1145/3099023.3099031","DOIUrl":"https://doi.org/10.1145/3099023.3099031","url":null,"abstract":"This demo paper presents a Web application for recommending travel regions for independent travelers. Users can specify preferences such as budget and preferred activities and receive suggested trips consisting of multiple regions. We explain the main ideas behind the data model and algorithm of our solution, and give an overview on the implementation","PeriodicalId":219391,"journal":{"name":"Adjunct Publication of the 25th Conference on User Modeling, Adaptation and Personalization","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125010216","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}
Effective exercise selection based on learner characteristics is important for Intelligent Tutoring Systems to improve learning. Based on a literature review, we categorize learner characteristics used for adaptation in an ITS. We then present a preliminary framework of the relationship between some of these learner characteristics, with an emphasis on personality, and how they can be used by an ITS to adapt exercise selection.
{"title":"Conceptualizing a Framework for Adaptive Exercise Selection with Personality as a Major Learner Characteristic","authors":"J. Okpo, J. Masthoff, Matt Dennis, N. Beacham","doi":"10.1145/3099023.3099078","DOIUrl":"https://doi.org/10.1145/3099023.3099078","url":null,"abstract":"Effective exercise selection based on learner characteristics is important for Intelligent Tutoring Systems to improve learning. Based on a literature review, we categorize learner characteristics used for adaptation in an ITS. We then present a preliminary framework of the relationship between some of these learner characteristics, with an emphasis on personality, and how they can be used by an ITS to adapt exercise selection.","PeriodicalId":219391,"journal":{"name":"Adjunct Publication of the 25th Conference on User Modeling, Adaptation and Personalization","volume":"300 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123392425","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}
This paper presents principles of designing Educational Assessment Technology (EdAT) that is culturally-appropriate. These principles are based on a study on international large-scale assessments (ILSA) and the relations of their findings with culture. In order to achieve this, we correlate ILSA data with Cultural Dimension data for 81 countries and examine the implications for designing student-centred systems based on cultural dimensions. Cultural dimensions such as long-term orientation are good predictors for achievement and can guide design decisions around adaptation.
{"title":"International Large-Scale Assessments and Culture: Implications for Designing Educational Technology","authors":"E. Kapros","doi":"10.1145/3099023.3099054","DOIUrl":"https://doi.org/10.1145/3099023.3099054","url":null,"abstract":"This paper presents principles of designing Educational Assessment Technology (EdAT) that is culturally-appropriate. These principles are based on a study on international large-scale assessments (ILSA) and the relations of their findings with culture. In order to achieve this, we correlate ILSA data with Cultural Dimension data for 81 countries and examine the implications for designing student-centred systems based on cultural dimensions. Cultural dimensions such as long-term orientation are good predictors for achievement and can guide design decisions around adaptation.","PeriodicalId":219391,"journal":{"name":"Adjunct Publication of the 25th Conference on User Modeling, Adaptation and Personalization","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128830609","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}
Our wider research project investigates the design of a persuasive game for preventing mental health problems and improving subjective wellbeing in a student population. In this paper, we explore how persuasive game elements and interactions can be adapted to different student personalities, active stressors and attitudes. In six focus groups we investigated (1) which key stressors are experienced by students, (2) what characteristics of students need to be considered for adapting game interactions and challenges, and (3) which approaches to personalisation could be applied. Participants were shown stories about a fictional student, conveying high and low levels of three personality traits (Conscientiousness, Emotional Stability and Extraversion), levels of active stressors, and varying attitudes towards change. Participants discussed how to tailor game interactions, activities and challenges to the characteristics of the fictional student. In general, participants perceived real-time personalisation using implicit measures as more effective, but recognised explicit profiling as a valuable complementary method. These findings have implications for the personalisation and design of persuasive game based interventions for health.
{"title":"Qualitative Study into Adapting Persuasive Games for Mental Wellbeing to Personality, Stressors and Attitudes","authors":"Ana Ciocarlan, J. Masthoff, N. Oren","doi":"10.1145/3099023.3099111","DOIUrl":"https://doi.org/10.1145/3099023.3099111","url":null,"abstract":"Our wider research project investigates the design of a persuasive game for preventing mental health problems and improving subjective wellbeing in a student population. In this paper, we explore how persuasive game elements and interactions can be adapted to different student personalities, active stressors and attitudes. In six focus groups we investigated (1) which key stressors are experienced by students, (2) what characteristics of students need to be considered for adapting game interactions and challenges, and (3) which approaches to personalisation could be applied. Participants were shown stories about a fictional student, conveying high and low levels of three personality traits (Conscientiousness, Emotional Stability and Extraversion), levels of active stressors, and varying attitudes towards change. Participants discussed how to tailor game interactions, activities and challenges to the characteristics of the fictional student. In general, participants perceived real-time personalisation using implicit measures as more effective, but recognised explicit profiling as a valuable complementary method. These findings have implications for the personalisation and design of persuasive game based interventions for health.","PeriodicalId":219391,"journal":{"name":"Adjunct Publication of the 25th Conference on User Modeling, Adaptation and Personalization","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127429211","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}
Mirjam Augstein, Thomas Neumayr, W. Kurschl, Daniel Kern, Thomas Burger, J. Altmann
Personalization has been discussed in a number of domains such as learning, search or information retrieval. In the area of human-computer interaction, personalization also plays a prominent role. For the variety of users, especially for those with impairments, interaction abilities vary drastically and for many it is not possible to use common interaction devices like a mouse or the touch screen of a smart phone (at least not with their predefined configuration). This paper describes a personalized interaction approach based on i) modeling users' interaction abilities, ii) automated selection and configuration of interaction devices for the individual user, and iii) adaptive behavior of selected applications. It is not exclusively focused on the requirements of people with impairments but frequently takes up this target group as it might particularly benefit from interaction personalization concepts.
{"title":"A Personalized Interaction Approach: Motivation and Use Case","authors":"Mirjam Augstein, Thomas Neumayr, W. Kurschl, Daniel Kern, Thomas Burger, J. Altmann","doi":"10.1145/3099023.3099051","DOIUrl":"https://doi.org/10.1145/3099023.3099051","url":null,"abstract":"Personalization has been discussed in a number of domains such as learning, search or information retrieval. In the area of human-computer interaction, personalization also plays a prominent role. For the variety of users, especially for those with impairments, interaction abilities vary drastically and for many it is not possible to use common interaction devices like a mouse or the touch screen of a smart phone (at least not with their predefined configuration). This paper describes a personalized interaction approach based on i) modeling users' interaction abilities, ii) automated selection and configuration of interaction devices for the individual user, and iii) adaptive behavior of selected applications. It is not exclusively focused on the requirements of people with impairments but frequently takes up this target group as it might particularly benefit from interaction personalization concepts.","PeriodicalId":219391,"journal":{"name":"Adjunct Publication of the 25th Conference on User Modeling, Adaptation and Personalization","volume":"77 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134352606","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}
Valentina Maccatrozzo, E. V. Everdingen, Lora Aroyo, G. Schreiber
In the digital era, personalisation systems are the typical way to deal with the massive amount of information on the Web. ese systems decide in our place what we like, possibly hiding us away from a complete world of potentially interesting content. ese systems do not challenge us to open our horizons of interest, trap- ping us more and more in our lter bubble. Introducing diversity and serendipity in the recommendation results has been widely recognised as the solution to this issue in the information retrieval eld. However, serendipity cannot be addressed and measured with traditional accuracy metrics, because it introduces much more complexity in terms of subjectivity and personality. Inspired by the curiosity theory of Berlyne, further developed by Silvia, we introduce in user pro les a so-called coping potential estimation as a measure of the users' ability to cope with new items (e.g., ability to appreciate serendipitous recommendations). Our assumption is that curiosity leads to serendipity and high coping potential users accept more serendipitous results, and thus we need to model it in the recommendation algorithm. We performed an online ex- periment where we asked users a number of questions about TV programmes recommendations. Our results show that users with a high coping potential are more inclined to accept serendipitous recommendations than their counterparts.
{"title":"Everybody, More or Less, likes Serendipity","authors":"Valentina Maccatrozzo, E. V. Everdingen, Lora Aroyo, G. Schreiber","doi":"10.1145/3099023.3099064","DOIUrl":"https://doi.org/10.1145/3099023.3099064","url":null,"abstract":"In the digital era, personalisation systems are the typical way to deal with the massive amount of information on the Web. ese systems decide in our place what we like, possibly hiding us away from a complete world of potentially interesting content. ese systems do not challenge us to open our horizons of interest, trap- ping us more and more in our lter bubble. Introducing diversity and serendipity in the recommendation results has been widely recognised as the solution to this issue in the information retrieval eld. However, serendipity cannot be addressed and measured with traditional accuracy metrics, because it introduces much more complexity in terms of subjectivity and personality. Inspired by the curiosity theory of Berlyne, further developed by Silvia, we introduce in user pro les a so-called coping potential estimation as a measure of the users' ability to cope with new items (e.g., ability to appreciate serendipitous recommendations). Our assumption is that curiosity leads to serendipity and high coping potential users accept more serendipitous results, and thus we need to model it in the recommendation algorithm. We performed an online ex- periment where we asked users a number of questions about TV programmes recommendations. Our results show that users with a high coping potential are more inclined to accept serendipitous recommendations than their counterparts.","PeriodicalId":219391,"journal":{"name":"Adjunct Publication of the 25th Conference on User Modeling, Adaptation and Personalization","volume":"114 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133880885","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}
Personalized educational systems are able to provide learners questions of specified difficulty. Since learners differ, the appropriate level of difficulty may vary and it may be impossible to find an universal setting. We implemented a version of an adaptive educational system for geography practice that allows learners to adjust difficulty of questions. We evaluated this feature using a randomized control experiment. The overall results show only a small effect of the adjustment. A more detailed analysis, however, shows that for some groups of learners the effect can be important, although not necessarily advantageous. The collected data from the experiment provide insight into how to tune question difficulty automatically.
{"title":"Should We Give Learners Control Over Item Difficulty?","authors":"Jan Papousek, Radek Pelánek","doi":"10.1145/3099023.3099080","DOIUrl":"https://doi.org/10.1145/3099023.3099080","url":null,"abstract":"Personalized educational systems are able to provide learners questions of specified difficulty. Since learners differ, the appropriate level of difficulty may vary and it may be impossible to find an universal setting. We implemented a version of an adaptive educational system for geography practice that allows learners to adjust difficulty of questions. We evaluated this feature using a randomized control experiment. The overall results show only a small effect of the adjustment. A more detailed analysis, however, shows that for some groups of learners the effect can be important, although not necessarily advantageous. The collected data from the experiment provide insight into how to tune question difficulty automatically.","PeriodicalId":219391,"journal":{"name":"Adjunct Publication of the 25th Conference on User Modeling, Adaptation and Personalization","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133896917","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}
Hanna Schäfer, Mehdi Elahi, David Elsweiler, Georg Groh, Morgan Harvey, Bernd Ludwig, F. Ricci, A. Said
In order to use and model nutritional knowledge in a food recommender system, uncertainties regarding the users nutritional state and thus the personal health value of food items, as well as conflicting nutritional theories need to be quantified, qualified and subsumed into falsifiable models. In this paper, we reflect on different error sources with respect to nutrition and consider how such issues can be tackled in future systems. We discuss the integration of general nutritional theories into information systems as well as user specific nutritional measures and different approaches to evaluating the utility of a given nutritional model.
{"title":"User Nutrition Modelling and Recommendation: Balancing Simplicity and Complexity","authors":"Hanna Schäfer, Mehdi Elahi, David Elsweiler, Georg Groh, Morgan Harvey, Bernd Ludwig, F. Ricci, A. Said","doi":"10.1145/3099023.3099108","DOIUrl":"https://doi.org/10.1145/3099023.3099108","url":null,"abstract":"In order to use and model nutritional knowledge in a food recommender system, uncertainties regarding the users nutritional state and thus the personal health value of food items, as well as conflicting nutritional theories need to be quantified, qualified and subsumed into falsifiable models. In this paper, we reflect on different error sources with respect to nutrition and consider how such issues can be tackled in future systems. We discuss the integration of general nutritional theories into information systems as well as user specific nutritional measures and different approaches to evaluating the utility of a given nutritional model.","PeriodicalId":219391,"journal":{"name":"Adjunct Publication of the 25th Conference on User Modeling, Adaptation and Personalization","volume":"123 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114522958","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}
Marco Polignano, Pierpaolo Basile, Gaetano Rossiello, M. Degemmis, G. Semeraro
The use of social media, like Facebook, Twitter and LinkedIn, is nowadays very common and quite for sure each one of us has at least a digital profile on them. The information left of these platforms such as likes, posts, tweets and photos are very informative and can be used for deducting our preferences, tendencies and behaviors. The analysis of the social media footprints has become a relevant research topic in the last decade and many works have demonstrated how to extract some traits of the user's affective sphere. In this paper, we focus on the prediction of empathic tendencies of a subject as an index of the influence of emotions during decisional processes. This value can be included in the user profile and can be relevant in some scenarios, such as music and movie recommender systems, where the emotional component is strongly delineated. We propose an approach of empathy level prediction based on a linear regression algorithm over Facebook profiles. We use a word2vec representation of the textual contents of the user's time-line posts, a LDA and SVD vector representation of the user's likes and other general descriptive data. The evaluation performed has demonstrated the validity of the approach for predicting the empathy tendency and the results have showed some relevant correlations with some specific groups of user's descriptive features.
{"title":"User's Social Media Profile as Predictor of Empathy","authors":"Marco Polignano, Pierpaolo Basile, Gaetano Rossiello, M. Degemmis, G. Semeraro","doi":"10.1145/3099023.3099103","DOIUrl":"https://doi.org/10.1145/3099023.3099103","url":null,"abstract":"The use of social media, like Facebook, Twitter and LinkedIn, is nowadays very common and quite for sure each one of us has at least a digital profile on them. The information left of these platforms such as likes, posts, tweets and photos are very informative and can be used for deducting our preferences, tendencies and behaviors. The analysis of the social media footprints has become a relevant research topic in the last decade and many works have demonstrated how to extract some traits of the user's affective sphere. In this paper, we focus on the prediction of empathic tendencies of a subject as an index of the influence of emotions during decisional processes. This value can be included in the user profile and can be relevant in some scenarios, such as music and movie recommender systems, where the emotional component is strongly delineated. We propose an approach of empathy level prediction based on a linear regression algorithm over Facebook profiles. We use a word2vec representation of the textual contents of the user's time-line posts, a LDA and SVD vector representation of the user's likes and other general descriptive data. The evaluation performed has demonstrated the validity of the approach for predicting the empathy tendency and the results have showed some relevant correlations with some specific groups of user's descriptive features.","PeriodicalId":219391,"journal":{"name":"Adjunct Publication of the 25th Conference on User Modeling, Adaptation and Personalization","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124388438","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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