The notion of intelligent systems that "care" is at the center of research in areas such as Intelligent Tutoring Systems and Adaptive Systems. This paper elaborates on the notion of caring assessment systems, and presents work towards achieving this vision that has potential for improving students' assessment experiences.
{"title":"Toward Caring Assessment Systems","authors":"Juan-Diego Zapata-Rivera","doi":"10.1145/3099023.3099106","DOIUrl":"https://doi.org/10.1145/3099023.3099106","url":null,"abstract":"The notion of intelligent systems that \"care\" is at the center of research in areas such as Intelligent Tutoring Systems and Adaptive Systems. This paper elaborates on the notion of caring assessment systems, and presents work towards achieving this vision that has potential for improving students' assessment experiences.","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":"130991178","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}
Recommender systems are used to suggest users products that they would not be able to find by themselves. State of the art algorithms assume that items have static features, however this assumption does not always correspond to reality. There are challenging and still unexplored domains, where not only users but also items have properties that evolve continuously over time. In this research we aim to overcome these limitations by suggesting to model evolution of users and items as a reinforcement learning problem. As use case we will refer to the recommendation problem applied to the financial domain, where items' (contracts) features evolve continuously according to "market laws".
{"title":"Modelling User Behaviors with Evolving Users and Catalogs of Evolving Items","authors":"Leonardo Cella","doi":"10.1145/3099023.3102251","DOIUrl":"https://doi.org/10.1145/3099023.3102251","url":null,"abstract":"Recommender systems are used to suggest users products that they would not be able to find by themselves. State of the art algorithms assume that items have static features, however this assumption does not always correspond to reality. There are challenging and still unexplored domains, where not only users but also items have properties that evolve continuously over time. In this research we aim to overcome these limitations by suggesting to model evolution of users and items as a reinforcement learning problem. As use case we will refer to the recommendation problem applied to the financial domain, where items' (contracts) features evolve continuously according to \"market laws\".","PeriodicalId":219391,"journal":{"name":"Adjunct Publication of the 25th Conference on User Modeling, Adaptation and Personalization","volume":"389 2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134323786","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}
In this paper we describe a preliminary investigation in using pupil dilation measurements to understand user visualization processing, with the long-term goal of building user-adaptive visualizations that can tailor the presentation of complex visual information to specific user needs and states. In particular, we look at how a selection of pupil dilation measurements are affected by adding several highlighting interventions designed to aid visualization processing to a bar graph.
{"title":"Leveraging Pupil Dilation Measures for Understanding Users' Cognitive Load During Visualization Processing","authors":"Dereck Toker, C. Conati","doi":"10.1145/3099023.3099059","DOIUrl":"https://doi.org/10.1145/3099023.3099059","url":null,"abstract":"In this paper we describe a preliminary investigation in using pupil dilation measurements to understand user visualization processing, with the long-term goal of building user-adaptive visualizations that can tailor the presentation of complex visual information to specific user needs and states. In particular, we look at how a selection of pupil dilation measurements are affected by adding several highlighting interventions designed to aid visualization processing to a bar graph.","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":"133761363","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}
In human computer interaction, some of the user activities are intentional, and other unintentional, but user interfaces are usually designed to react only to intentional commands. However, user's unintentional activity contains many clues about a user, that can be beneficial to take into account in designing appropriate response. Current study focuses on these unintentional traces, that left behind by use of standard input devices, keyboard and mouse, and specifically, we try to predict users age and gender. Mouse and keyboard data used in this study, are collected in six different systems between 2011 and 2017 in total from 1519 subjects. Some supervised machine learning models yield to f-scores over 0.9 when predicted both user age or gender.
{"title":"Predicting Age and Gender by Keystroke Dynamics and Mouse Patterns","authors":"Avar Pentel","doi":"10.1145/3099023.3099105","DOIUrl":"https://doi.org/10.1145/3099023.3099105","url":null,"abstract":"In human computer interaction, some of the user activities are intentional, and other unintentional, but user interfaces are usually designed to react only to intentional commands. However, user's unintentional activity contains many clues about a user, that can be beneficial to take into account in designing appropriate response. Current study focuses on these unintentional traces, that left behind by use of standard input devices, keyboard and mouse, and specifically, we try to predict users age and gender. Mouse and keyboard data used in this study, are collected in six different systems between 2011 and 2017 in total from 1519 subjects. Some supervised machine learning models yield to f-scores over 0.9 when predicted both user age or gender.","PeriodicalId":219391,"journal":{"name":"Adjunct Publication of the 25th Conference on User Modeling, Adaptation and Personalization","volume":"71 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":"132901240","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 current approaches and open issues regarding the modeling of users' physical activity when learning motor skills, such as those required to dance, play a musical instrument, practice sports or train in martial arts. On the one hand, it reveals the lack of personalized psychomotor learning systems and how the modeling of users' physical activity is just now becoming part of UMAP (User Modeling, Adaptation and Personalization) community research agenda. On the other hand, it proposes the Labanotation as a way for describing the movements performed during the users' physical activity, and comments on related works which show that it seems to be feasible to perform this labeling automatically with machine learning techniques. To touch down the proposal, the applicability of Labanotation for modeling the psychomotor activity when learning defensive martial arts movements such as those performed jointly in pairs in Aikido is analyzed.
{"title":"Modeling Psychomotor Activity: Current Approaches and Open Issues","authors":"O. Santos, Martha H. Eddy","doi":"10.1145/3099023.3099083","DOIUrl":"https://doi.org/10.1145/3099023.3099083","url":null,"abstract":"This paper presents current approaches and open issues regarding the modeling of users' physical activity when learning motor skills, such as those required to dance, play a musical instrument, practice sports or train in martial arts. On the one hand, it reveals the lack of personalized psychomotor learning systems and how the modeling of users' physical activity is just now becoming part of UMAP (User Modeling, Adaptation and Personalization) community research agenda. On the other hand, it proposes the Labanotation as a way for describing the movements performed during the users' physical activity, and comments on related works which show that it seems to be feasible to perform this labeling automatically with machine learning techniques. To touch down the proposal, the applicability of Labanotation for modeling the psychomotor activity when learning defensive martial arts movements such as those performed jointly in pairs in Aikido is analyzed.","PeriodicalId":219391,"journal":{"name":"Adjunct Publication of the 25th Conference on User Modeling, Adaptation and Personalization","volume":"17 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":"132509366","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}
Gamification has been used in a variety of application domains to promote behaviour change. Nevertheless, the mechanisms behind it are still not fully understood. Recent empirical results have shown that personalized approaches can potentially achieve better results than generic approaches. However, we still lack a general framework for building personalized gameful applications. To address this gap, we present a novel general framework for personalized gameful applications using recommender systems (i.e., software tools and technologies to recommend suggestions to users that they might enjoy). This framework contributes to understanding and building effective persuasive and gameful applications by describing the different building blocks of a recommender system (users, items, and transactions) in a personalized gamification context.
{"title":"Recommender Systems for Personalized Gamification","authors":"G. F. Tondello, Rita Orji, L. Nacke","doi":"10.1145/3099023.3099114","DOIUrl":"https://doi.org/10.1145/3099023.3099114","url":null,"abstract":"Gamification has been used in a variety of application domains to promote behaviour change. Nevertheless, the mechanisms behind it are still not fully understood. Recent empirical results have shown that personalized approaches can potentially achieve better results than generic approaches. However, we still lack a general framework for building personalized gameful applications. To address this gap, we present a novel general framework for personalized gameful applications using recommender systems (i.e., software tools and technologies to recommend suggestions to users that they might enjoy). This framework contributes to understanding and building effective persuasive and gameful applications by describing the different building blocks of a recommender system (users, items, and transactions) in a personalized gamification context.","PeriodicalId":219391,"journal":{"name":"Adjunct Publication of the 25th Conference on User Modeling, Adaptation and Personalization","volume":"50 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":"116513684","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}
Test collections for offline evaluation remain crucial for information retrieval research and industrial practice, yet reusability of test collections is under threat by different factors such as dynamic nature of data collections and new trends in building retrieval systems. Specifically, building reusable test collections that last over years is a very challenging problem as retrieval approaches change considerably per year based on new trends among Information Retrieval researchers. We experiment with a novel temporal reusability test to evaluate reusability of test collections over a year based on leaving mutual topics in experiment, in which we borrow some judged topics from previous years and include them in the new set of topics to be used in the current year. In fact, we experiment whether a new set of retrieval systems can be evaluated and comparatively ranked based on an old test collection. Our experiments is done based on two sets of runs from Text REtrieval Conference (TREC) 2015 and 2016 Contextual Suggestion Track, which is a personalized venue recommendation task. Our experiments show that the TREC 2015 test collection is not temporally reusable. The test collection should be used with extreme care based on early precision metrics and slightly less care based on NDCG, bpref and MAP metrics. Our approach offers a very precise experiment to test temporal reusability of test collections over a year, and it is very effective to be used in tracks running a setup similar to their previous years.
{"title":"On the Reusability of Personalized Test Collections","authors":"Seyyed Hadi Hashemi, J. Kamps","doi":"10.1145/3099023.3099044","DOIUrl":"https://doi.org/10.1145/3099023.3099044","url":null,"abstract":"Test collections for offline evaluation remain crucial for information retrieval research and industrial practice, yet reusability of test collections is under threat by different factors such as dynamic nature of data collections and new trends in building retrieval systems. Specifically, building reusable test collections that last over years is a very challenging problem as retrieval approaches change considerably per year based on new trends among Information Retrieval researchers. We experiment with a novel temporal reusability test to evaluate reusability of test collections over a year based on leaving mutual topics in experiment, in which we borrow some judged topics from previous years and include them in the new set of topics to be used in the current year. In fact, we experiment whether a new set of retrieval systems can be evaluated and comparatively ranked based on an old test collection. Our experiments is done based on two sets of runs from Text REtrieval Conference (TREC) 2015 and 2016 Contextual Suggestion Track, which is a personalized venue recommendation task. Our experiments show that the TREC 2015 test collection is not temporally reusable. The test collection should be used with extreme care based on early precision metrics and slightly less care based on NDCG, bpref and MAP metrics. Our approach offers a very precise experiment to test temporal reusability of test collections over a year, and it is very effective to be used in tracks running a setup similar to their previous years.","PeriodicalId":219391,"journal":{"name":"Adjunct Publication of the 25th Conference on User Modeling, Adaptation and Personalization","volume":"18 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":"129920825","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}
In this paper, we explore the question "would people be willing to share their personal data in exchange for highly-personalized online ads?" through a Wizard-of-Oz deception study. Our volunteers were exposed via a web browser to three different highly- personalized ads, designed by people who knew them well. They were made believe that the ads had been generated automatically by an Artificial Intelligence engine on the basis of their browsing & location history and/or personal traits. The participants' reactions were surprisingly favorable: in more than 50% of the cases, the ads triggered spontaneous positive emotional reactions; almost 90% of participants would share at least two of the three data sources with advertisers; and about 50% would share all data sources. Our results provide evidence that highly-personalized ads may offset the concerns that people have about sharing their personal data. Thus further efforts in building increasingly personalized online ads would represent a worthwhile endeavour.
{"title":"\"OMG! How did it know that?\": Reactions to Highly-Personalized Ads","authors":"A. Matic, M. Pielot, N. Oliver","doi":"10.1145/3099023.3101411","DOIUrl":"https://doi.org/10.1145/3099023.3101411","url":null,"abstract":"In this paper, we explore the question \"would people be willing to share their personal data in exchange for highly-personalized online ads?\" through a Wizard-of-Oz deception study. Our volunteers were exposed via a web browser to three different highly- personalized ads, designed by people who knew them well. They were made believe that the ads had been generated automatically by an Artificial Intelligence engine on the basis of their browsing & location history and/or personal traits. The participants' reactions were surprisingly favorable: in more than 50% of the cases, the ads triggered spontaneous positive emotional reactions; almost 90% of participants would share at least two of the three data sources with advertisers; and about 50% would share all data sources. Our results provide evidence that highly-personalized ads may offset the concerns that people have about sharing their personal data. Thus further efforts in building increasingly personalized online ads would represent a worthwhile endeavour.","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":"131271788","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}
M. Kravcík, O. Santos, J. Boticario, M. Bieliková, Tomáš Horváth
Personalization approaches in learning environments can help to foster effective, efficient, and satisfactory learning. The focus of the PALE workshop series is on the different perspectives in which personalization can be addressed in learning environments. It offers an opportunity to present and discuss a wide spectrum of issues and solutions. In particular, this seventh edition includes seven papers dealing with adaptive exercise selection, personality, learning styles, control over item difficulty, tag recommendation, interactive presentation platform, psychomotor activity modeling, as well as affective computing.
{"title":"UMAP 2017 PALE Workshop Organizers' Welcome","authors":"M. Kravcík, O. Santos, J. Boticario, M. Bieliková, Tomáš Horváth","doi":"10.1145/3099023.3099077","DOIUrl":"https://doi.org/10.1145/3099023.3099077","url":null,"abstract":"Personalization approaches in learning environments can help to foster effective, efficient, and satisfactory learning. The focus of the PALE workshop series is on the different perspectives in which personalization can be addressed in learning environments. It offers an opportunity to present and discuss a wide spectrum of issues and solutions. In particular, this seventh edition includes seven papers dealing with adaptive exercise selection, personality, learning styles, control over item difficulty, tag recommendation, interactive presentation platform, psychomotor activity modeling, as well as affective computing.","PeriodicalId":219391,"journal":{"name":"Adjunct Publication of the 25th Conference on User Modeling, Adaptation and Personalization","volume":"10 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":"124403990","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}
Group Recommendation Systems (GRS) are personalization systems that provide recommendations to groups of people considering the initial preferences of each group's member, with the aim to maximize the satisfaction of the whole group. Since recent psychological studies evidence that people's satisfaction is influenced by the satisfaction of other people with whom they perform an activity, it is important to consider human aspects and social characteristics that affect the changes in individual's satisfactions in the recommendations generation process. In this work, we start an experimental analysis on how ties' strength and possible conflicts in a relationship can influence the individual's satisfactions, with the aim to derive a model that can be used to adapt individual utilities to the "Group Context" before aggregating them into the group's ones. Our hypothesis is that there is a direct correlation between tie strength and positive shifting, but the presence of conflict, instead, can lead to a negative influence, causing a drifting further apart between people's satisfactions. Results confirm these hypotheses, but also suggest that these two factors are not enough to define a general model and that other factors must be considered.
{"title":"A Detailed Analysis of the Impact of Tie Strength and Conflicts on Social Influence","authors":"F. Barile, J. Masthoff, Silvia Rossi","doi":"10.1145/3099023.3099056","DOIUrl":"https://doi.org/10.1145/3099023.3099056","url":null,"abstract":"Group Recommendation Systems (GRS) are personalization systems that provide recommendations to groups of people considering the initial preferences of each group's member, with the aim to maximize the satisfaction of the whole group. Since recent psychological studies evidence that people's satisfaction is influenced by the satisfaction of other people with whom they perform an activity, it is important to consider human aspects and social characteristics that affect the changes in individual's satisfactions in the recommendations generation process. In this work, we start an experimental analysis on how ties' strength and possible conflicts in a relationship can influence the individual's satisfactions, with the aim to derive a model that can be used to adapt individual utilities to the \"Group Context\" before aggregating them into the group's ones. Our hypothesis is that there is a direct correlation between tie strength and positive shifting, but the presence of conflict, instead, can lead to a negative influence, causing a drifting further apart between people's satisfactions. Results confirm these hypotheses, but also suggest that these two factors are not enough to define a general model and that other factors must be considered.","PeriodicalId":219391,"journal":{"name":"Adjunct Publication of the 25th Conference on User Modeling, Adaptation and Personalization","volume":"124 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":"114663441","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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