The raising demands on qualification increase the importance of technology as a facilitator in the educational process on the side of both receivers and providers. Beside the cognitive aspects, also metacognitive, emotional and motivational ones play a crucial role in learning. A challenge is to recognize the affective status of participants and react to them accordingly, in order to make the learning experience effective and efficient. Various approaches were investigated and reported in the literature. In order to develop mentoring support at the university level in concrete settings, we researched them and tried to identify the key requirements for our solution. Based on these requirements, we plan to design intelligent knowledge services for scalable mentoring processes.
{"title":"Towards Requirements for Intelligent Mentoring Systems","authors":"M. Kravcík, Katharina Schmid, C. Igel","doi":"10.1145/3345002.3349290","DOIUrl":"https://doi.org/10.1145/3345002.3349290","url":null,"abstract":"The raising demands on qualification increase the importance of technology as a facilitator in the educational process on the side of both receivers and providers. Beside the cognitive aspects, also metacognitive, emotional and motivational ones play a crucial role in learning. A challenge is to recognize the affective status of participants and react to them accordingly, in order to make the learning experience effective and efficient. Various approaches were investigated and reported in the literature. In order to develop mentoring support at the university level in concrete settings, we researched them and tried to identify the key requirements for our solution. Based on these requirements, we plan to design intelligent knowledge services for scalable mentoring processes.","PeriodicalId":153835,"journal":{"name":"Proceedings of the 23rd International Workshop on Personalization and Recommendation on the Web and Beyond","volume":"61 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115986847","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}
1 BACKGROUND The personalized selection and presentation of content have become common in today’s online world, for example on media streaming sites, e-commerce shops, and social networks. This automated personalization is often accomplished by recommender systems, which continuously collect and interpret information about the individual user. To determine which information items should be presented, these systems typically rely on machine learning. Over the last decades, a large variety of machine learning techniques of increasing complexity have been applied for building recommender systems. The recommendation models that are learned by such modern algorithms are, however, usually seen as black boxes. Technically, they often consist of values for hundreds or thousands of variables, making it impossible to provide a humanunderstandable rationale why a certain item is recommended to a particular user. Providing users with an explanation or at least with an intuition why an item is recommended can, however, be crucial, both for the acceptance of an individual recommendation and for the establishment of user trust towards the system as a whole [3]. Furthermore, such system-provided explanations can not only contribute to the acceptance of the system, but also serve as entry points for interactive approaches that allow users to give feedback as a means to correct system assumptions and, thus, take control of the recommendation process.
{"title":"Explanations and User Control in Recommender Systems","authors":"D. Jannach, Michael Jugovac, Ingrid Nunes","doi":"10.1145/3345002.3349293","DOIUrl":"https://doi.org/10.1145/3345002.3349293","url":null,"abstract":"1 BACKGROUND The personalized selection and presentation of content have become common in today’s online world, for example on media streaming sites, e-commerce shops, and social networks. This automated personalization is often accomplished by recommender systems, which continuously collect and interpret information about the individual user. To determine which information items should be presented, these systems typically rely on machine learning. Over the last decades, a large variety of machine learning techniques of increasing complexity have been applied for building recommender systems. The recommendation models that are learned by such modern algorithms are, however, usually seen as black boxes. Technically, they often consist of values for hundreds or thousands of variables, making it impossible to provide a humanunderstandable rationale why a certain item is recommended to a particular user. Providing users with an explanation or at least with an intuition why an item is recommended can, however, be crucial, both for the acceptance of an individual recommendation and for the establishment of user trust towards the system as a whole [3]. Furthermore, such system-provided explanations can not only contribute to the acceptance of the system, but also serve as entry points for interactive approaches that allow users to give feedback as a means to correct system assumptions and, thus, take control of the recommendation process.","PeriodicalId":153835,"journal":{"name":"Proceedings of the 23rd International Workshop on Personalization and Recommendation on the Web and Beyond","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122538706","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}
Workplace learning has been a part of our lives for a long time already. However, new technological opportunities can radically change not only formal, but also informal (unintentional) learning, which is typical for the workplace. Nowadays companies face a new challenge: the transition towards Industry 4.0. In this regard, information technology should support the whole spectrum of educational methodologies, including personalized guidance, collaborative learning, training of practical skills, as well as meta-cognitive scaffolding.
{"title":"Adaptive Workplace Learning Assistance","authors":"M. Kravcík","doi":"10.1145/3345002.3349294","DOIUrl":"https://doi.org/10.1145/3345002.3349294","url":null,"abstract":"Workplace learning has been a part of our lives for a long time already. However, new technological opportunities can radically change not only formal, but also informal (unintentional) learning, which is typical for the workplace. Nowadays companies face a new challenge: the transition towards Industry 4.0. In this regard, information technology should support the whole spectrum of educational methodologies, including personalized guidance, collaborative learning, training of practical skills, as well as meta-cognitive scaffolding.","PeriodicalId":153835,"journal":{"name":"Proceedings of the 23rd International Workshop on Personalization and Recommendation on the Web and Beyond","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122892798","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}
Parisa Shayan, R. Rondinelli, Menno van Zaanen, M. Atzmüller
Learning Management Systems (LMSs) play a significant role in educational technology. In this paper, we analyze different approaches in order to investigate the acceptance of an LMS. Utilizing questionnaire information structured on the Technology Acceptance Model (TAM), we apply descriptive network modeling and analysis complementing basic statistical analysis in order to identify specific patterns in the user data. We present the applied analysis methodology in detail, and demonstrate the connection to user modeling:here, descriptive statistics indicate student satisfaction with the usage (acceptance level) as a whole; network analysis indicates the level of variability w.r.t. the user questions, while specific patterns or motifs show the satisfaction levels for the different networks.
{"title":"Descriptive Network Modeling and Analysis for Investigating User Acceptance in a Learning Management System Context","authors":"Parisa Shayan, R. Rondinelli, Menno van Zaanen, M. Atzmüller","doi":"10.1145/3345002.3349288","DOIUrl":"https://doi.org/10.1145/3345002.3349288","url":null,"abstract":"Learning Management Systems (LMSs) play a significant role in educational technology. In this paper, we analyze different approaches in order to investigate the acceptance of an LMS. Utilizing questionnaire information structured on the Technology Acceptance Model (TAM), we apply descriptive network modeling and analysis complementing basic statistical analysis in order to identify specific patterns in the user data. We present the applied analysis methodology in detail, and demonstrate the connection to user modeling:here, descriptive statistics indicate student satisfaction with the usage (acceptance level) as a whole; network analysis indicates the level of variability w.r.t. the user questions, while specific patterns or motifs show the satisfaction levels for the different networks.","PeriodicalId":153835,"journal":{"name":"Proceedings of the 23rd International Workshop on Personalization and Recommendation on the Web and Beyond","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115848991","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}
The BBC is one of the world's leading broadcasters, producing a large amount of content for different audiences. Data-driven recommendations are a successful approach to increase user engagement providing tailored content and personalising their experience. However, concerns have been raised with regards to their effects on diversity and reinforcement of existing bias. Addressing these concerns is especially important for the BBC, whose values include trust, diversity, and impartiality. This position paper lays out the strategy followed by the BBC to develop automated recommendation systems, presenting our approach to create accurate, fair, and responsible recommendation systems.
{"title":"Data-Driven Recommendations in a Public Service Organisation","authors":"A. Piscopo, Maria Panteli, D. Penna","doi":"10.1145/3345002.3349286","DOIUrl":"https://doi.org/10.1145/3345002.3349286","url":null,"abstract":"The BBC is one of the world's leading broadcasters, producing a large amount of content for different audiences. Data-driven recommendations are a successful approach to increase user engagement providing tailored content and personalising their experience. However, concerns have been raised with regards to their effects on diversity and reinforcement of existing bias. Addressing these concerns is especially important for the BBC, whose values include trust, diversity, and impartiality. This position paper lays out the strategy followed by the BBC to develop automated recommendation systems, presenting our approach to create accurate, fair, and responsible recommendation systems.","PeriodicalId":153835,"journal":{"name":"Proceedings of the 23rd International Workshop on Personalization and Recommendation on the Web and Beyond","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114973782","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}
J. Abascal, Xabier Gardeazabal, J. Pérez, Xabier Valencia, O. Arbelaitz, J. Muguerza, Ainhoa Yera
Computer applications provide people with disabilities with unique opportunities for interpersonal communication, social interaction and active participation. However, rigid user interfaces often present accessibility barriers to people with physical, sensory or cognitive impairments. User interface personalization is crucial to overcome these barriers, enabling computer access to a considerable section of the population with disabilities. Adapting the user interface to people with disabilities requires taking into consideration their physical, sensory or cognitive abilities and restrictions and hence providing alternative access procedures according to their capacities. In the chapter 15, "Personalizing the User Interface for People with Disabilities" [1], we present methods and techniques that are being applied to research and practice on user interface personalization for people with disabilities. In addition, we discuss possible approaches for diverse application fields where personalization is required: accessibility to the web using transcoding, web mining for eGovernment, and human-robot interaction for people with severe motor restrictions.
{"title":"Personalizing the User Interface for People with Disabilities","authors":"J. Abascal, Xabier Gardeazabal, J. Pérez, Xabier Valencia, O. Arbelaitz, J. Muguerza, Ainhoa Yera","doi":"10.1145/3345002.3349292","DOIUrl":"https://doi.org/10.1145/3345002.3349292","url":null,"abstract":"Computer applications provide people with disabilities with unique opportunities for interpersonal communication, social interaction and active participation. However, rigid user interfaces often present accessibility barriers to people with physical, sensory or cognitive impairments. User interface personalization is crucial to overcome these barriers, enabling computer access to a considerable section of the population with disabilities. Adapting the user interface to people with disabilities requires taking into consideration their physical, sensory or cognitive abilities and restrictions and hence providing alternative access procedures according to their capacities. In the chapter 15, \"Personalizing the User Interface for People with Disabilities\" [1], we present methods and techniques that are being applied to research and practice on user interface personalization for people with disabilities. In addition, we discuss possible approaches for diverse application fields where personalization is required: accessibility to the web using transcoding, web mining for eGovernment, and human-robot interaction for people with severe motor restrictions.","PeriodicalId":153835,"journal":{"name":"Proceedings of the 23rd International Workshop on Personalization and Recommendation on the Web and Beyond","volume":"199 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116504185","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 advertisements are the price we have to pay for free social media platforms. Various studies have been carried out on user acceptance of such advertisements in general and most countries have adopted laws and regulations with respect to privacy and data protection. However, not all advertisements evoke the same responses: some ads are considered more annoying, intrusive or creepy than others. In this paper, we present the results of an observational study on user responses to actual Facebook advertisements. The results show that mismatches in terms of context, unexpected data collection or inference, overly generic explanations and repetition are common causes of anxiety and distrust.
{"title":"Unexpected and Unpredictable: Factors That Make Personalized Advertisements Creepy","authors":"E. Herder, Boping Zhang","doi":"10.1145/3345002.3349285","DOIUrl":"https://doi.org/10.1145/3345002.3349285","url":null,"abstract":"Personalized advertisements are the price we have to pay for free social media platforms. Various studies have been carried out on user acceptance of such advertisements in general and most countries have adopted laws and regulations with respect to privacy and data protection. However, not all advertisements evoke the same responses: some ads are considered more annoying, intrusive or creepy than others. In this paper, we present the results of an observational study on user responses to actual Facebook advertisements. The results show that mismatches in terms of context, unexpected data collection or inference, overly generic explanations and repetition are common causes of anxiety and distrust.","PeriodicalId":153835,"journal":{"name":"Proceedings of the 23rd International Workshop on Personalization and Recommendation on the Web and Beyond","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128924536","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. Atzmüller, Çiçek Güven, Spyroula Masiala, Rick Mackenbach, Parisa Shayan, Werner Liebregts
This paper investigates socio-spatial interaction networks for user modeling: We analyze preferences and perceptions of socio-proximity human interactions in relation to the observed interactions. The analysis is performed on a real-world dataset capturing interaction networks using wearable sensors coupled with self-report questionnaires about preferences and perception of those interactions.
{"title":"Behavioral Analysis on Socio-Spatial Interaction Networks concerning User Preferences, Interactions and their Perception","authors":"M. Atzmüller, Çiçek Güven, Spyroula Masiala, Rick Mackenbach, Parisa Shayan, Werner Liebregts","doi":"10.1145/3345002.3349291","DOIUrl":"https://doi.org/10.1145/3345002.3349291","url":null,"abstract":"This paper investigates socio-spatial interaction networks for user modeling: We analyze preferences and perceptions of socio-proximity human interactions in relation to the observed interactions. The analysis is performed on a real-world dataset capturing interaction networks using wearable sensors coupled with self-report questionnaires about preferences and perception of those interactions.","PeriodicalId":153835,"journal":{"name":"Proceedings of the 23rd International Workshop on Personalization and Recommendation on the Web and Beyond","volume":"114 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116674227","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}
Mobile proactive tourist recommender systems can support tourists by recommending the best choice depending on different contexts related to themselves and the environment. In this paper, we propose to utilize wearable sensors to gather health information about a tourist and use them for recommending activities. We discuss a range of wearable devices, sensors to infer physiological conditions of the users, and exemplify the feasibility using a popular self-quantification mobile app. Our main contribution is a data model to derive relations between the parameters measured by the wearable sensors, such as heart rate, body temperature, blood pressure, and use them to infer the physiological condition of a user. This model can then be used to derive classes of tourist activities that determine which items should be recommended.
{"title":"Modeling Physiological Conditions for Proactive Tourist Recommendations","authors":"Rinita Roy, Linus W. Dietz","doi":"10.1145/3345002.3349289","DOIUrl":"https://doi.org/10.1145/3345002.3349289","url":null,"abstract":"Mobile proactive tourist recommender systems can support tourists by recommending the best choice depending on different contexts related to themselves and the environment. In this paper, we propose to utilize wearable sensors to gather health information about a tourist and use them for recommending activities. We discuss a range of wearable devices, sensors to infer physiological conditions of the users, and exemplify the feasibility using a popular self-quantification mobile app. Our main contribution is a data model to derive relations between the parameters measured by the wearable sensors, such as heart rate, body temperature, blood pressure, and use them to infer the physiological condition of a user. This model can then be used to derive classes of tourist activities that determine which items should be recommended.","PeriodicalId":153835,"journal":{"name":"Proceedings of the 23rd International Workshop on Personalization and Recommendation on the Web and Beyond","volume":"28 5","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132532508","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}
{"title":"Proceedings of the 23rd International Workshop on Personalization and Recommendation on the Web and Beyond","authors":"","doi":"10.1145/3345002","DOIUrl":"https://doi.org/10.1145/3345002","url":null,"abstract":"","PeriodicalId":153835,"journal":{"name":"Proceedings of the 23rd International Workshop on Personalization and Recommendation on the Web and Beyond","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124553068","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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