Adjunct Proceedings of the 30th ACM Conference on User Modeling, Adaptation and Personalization最新文献

{"title":"A Mobile Guide to Explore Interconnections between Science, Art and Territory","authors":"Noemi Mauro, Angelo Geninatti Cossatin, Ester Cravero, L. Ardissono, Guido Magnano, M. Giardino, Claudio Mattutino","doi":"10.1145/3511047.3537649","DOIUrl":"https://doi.org/10.1145/3511047.3537649","url":null,"abstract":"Most Cultural Heritage mobile guides are developed using a location-based hypertext model that guides the exploration of individual itineraries. However, Cultural Heritage places are often immersed in parallel and interlaced stories about art, history and science, which might be relevant to the tourist. Therefore, we are interested in investigating the semantic connections between the narratives that involve different Point of Interests to provide tourists with interconnected views of the Cultural Heritage of a place. As a first step in this direction, we present the Triangolazioni mobile guide, which allows users to thematically explore places through the navigation of narratives, and to connect narratives to each other based on topic similarity. Triangolazioni can be accessed from mobile phone, tablet and desktop, and supports the physical and virtual exploration of Points of Interest in the area around Torino, Italy.","PeriodicalId":162466,"journal":{"name":"Adjunct Proceedings of the 30th ACM Conference on User Modeling, Adaptation and Personalization","volume":"59 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126079687","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":"Free of Walls: Participatory Design of an Out-World Experience via Virtual Reality for Dementia In-patients","authors":"M. Matsangidou, Fotos Frangoudes, Theodoros Solomou, Ersi Papayianni, Constantinos S. Pattichis","doi":"10.1145/3511047.3537651","DOIUrl":"https://doi.org/10.1145/3511047.3537651","url":null,"abstract":"Many people with dementia residing in long-term care may face barriers in accessing experiences beyond their physical premises; this may be due to location, mobility constraints, legal act and/or mental health restrictions. Previous research has suggested that institutionalization increases the co-existing symptoms of dementia, such as aggression, depression, apathy, lack of motivation and loss of interest in oneself and others. Despite the importance of supporting the mental well-being of people with dementia, in many cases, it remains undertreated. In recent years, there has been growing research interest in designing non-pharmacological interventions aiming to improve the Health-Related Quality of Life for people with dementia within long-term care. With computer technology and especially Virtual Reality offering endless opportunities for mental support, we must consider how Virtual Reality for people with dementia can be sensitively designed to provide comfortable, enriching out-world experiences. Working closely with 24 dementia patients and 51 medical and paramedical personnel, we co-designed an intelligent and personalized Virtual Reality system to enhance symptom management of dementia patients residing in long-term care. Through this paper, we thoroughly explain the screening process and analysis we run to identify which environments patients would like to receive as a Virtual Reality intervention to minimize the aforementioned co-existing symptoms of dementia, and the development of an intelligent system using the selected environments, that adapts the content of the Virtual Reality experience based on physiological and eye-tracking data from the patients and their personal preferences.","PeriodicalId":162466,"journal":{"name":"Adjunct Proceedings of the 30th ACM Conference on User Modeling, Adaptation and Personalization","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122044305","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":"The META4RS Proposal: Museum Emotion and Tracking Analysis For Recommender Systems","authors":"Alessio Ferrato, Carla Limongelli, M. Mezzini, G. Sansonetti","doi":"10.1145/3511047.3537664","DOIUrl":"https://doi.org/10.1145/3511047.3537664","url":null,"abstract":"In this paper, we present the rationale and the ideas behind META4RS, a museum itinerary recommender system. The system leverages deep learning techniques to acquire data about the visitor’s position while ensuring her anonymity. Moreover, the visitor’s appraisal of the artwork she observes is inferred implicitly based on the emotional reactions she expresses while watching a given artwork. We are not aware of any such recommender system proposed in the research literature. However, this system should ensure several advantages: (i) it is non-intrusive since it makes use of simple badges and off-the-shelf cameras while ensuring the anonymity of the visitor; (ii) it is independent of the type of museum; (iii) it offers personalized itineraries to visitors based on their implicitly inferred interests and preferences. Specifically, we illustrate the background and describe the architecture of the proposed system, discussing the steps required for its implementation. We also provide details of what has already been done and what remains to be done, outlining the open problems.","PeriodicalId":162466,"journal":{"name":"Adjunct Proceedings of the 30th ACM Conference on User Modeling, Adaptation and Personalization","volume":"10 3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126246943","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":"Forming Teams of Learners Online in a User as Wizard Study with Openness, Conscientiousness, and cognitive Ability","authors":"F. Vinella, S.T.T. Koppelaar, J. Masthoff","doi":"10.1145/3511047.3537660","DOIUrl":"https://doi.org/10.1145/3511047.3537660","url":null,"abstract":"Forming teams of learners is a task that presents numerous challenges for educators increasingly relying on automated tools to optimize the process. The problem increases in difficulty in online classroom settings, where educators have little familiarity with the students. In this work, we present a User as Wizard study where 108 online crowd participants formed four teams of three teammates each from a pool of twelve dummy learner profiles. The profiles contained information about the learners’ Conscientiousness, Openness, and cognitive ability levels. These attributes were derived from a pre-study with a smaller sample of crowd participants (N=52) rating the relevance of the Big Five personality traits and cognitive ability in team formation for educational purposes. The User as Wizard study shows that most people tend to form within (meaning most attributes of the teammates even out) and between (meaning the teams have similar attributes averages) balanced teams. It also shows that people perceive Conscientiousness and Openness as two of the most relevant personality traits when profiling learners for team formation. We compare these results to the probability of them being random and discuss the findings in the light of human-centered modeling of system designs and automation in education.","PeriodicalId":162466,"journal":{"name":"Adjunct Proceedings of the 30th ACM Conference on User Modeling, Adaptation and Personalization","volume":"57 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130886415","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":"5th Workshop on Fairness in User Modeling, Adaptation, and Personalization (FairUMAP 2022)","authors":"S. Kleanthous, B. Mobasher, T. Kuflik, Bettina Berendt, R. Burke, Jahna Otterbacher, Nasim Sonboli, Avital Shulner-Tal","doi":"10.1145/3511047.3536348","DOIUrl":"https://doi.org/10.1145/3511047.3536348","url":null,"abstract":"ACM Reference Format: Styliani Kleanthous, Bamshad Mobasher, Tsvika Kuflik, Bettina Berendt, Robin Burke, Jahna Otterbacher, Nasim Sonboli, and Avital Shulner Tal. 2022. 5th Workshop on Fairness in User Modeling, Adaptation, and Personalization (FairUMAP 2022). In Adjunct Proceedings of the 30th ACM Conference on User Modeling, Adaptation and Personalization (UMAP ’22 Adjunct), July 4–7, 2022, Barcelona, Spain. ACM, New York, NY, USA, Article 111, 2 pages. https://doi.org/10.1145/3511047.3536348","PeriodicalId":162466,"journal":{"name":"Adjunct Proceedings of the 30th ACM Conference on User Modeling, Adaptation and Personalization","volume":"57 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114862002","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":"Using Recommender Systems to Help Revitalize Local News","authors":"Payam Pourashraf, B. Mobasher","doi":"10.1145/3511047.3536411","DOIUrl":"https://doi.org/10.1145/3511047.3536411","url":null,"abstract":"American local newspapers have been experiencing a large loss of reader retention and business within the past 15 years due to the proliferation of online news sources and social media. This has led to a disturbing trend where local journalism and local news outlets are being forced out of business often leaving whole communities without a key source of credible information. This trend has a potentially broad societal impact as these key anchors of local trust and democracy are slowly becoming extinct. Local media companies are starting to shift from an advertising-supported business model to one based on subscriptions to mitigate their financial crises. But with strong competition from a variety of online news sources, these companies need to increase user engagement by providing significant added value. Providing more personalized content in the local context may be one way that these companies can succeed in this effort. Recommender system technologies are the primary enabling mechanisms for delivering such personalized content. However, using standard machine learning models that focus on users’ global preferences is not appropriate in this context because the local preferences of users exhibit some specific characteristics which do not necessarily match their long-term or global preferences in the news. The overall goal of this research is to develop predictive models that more effectively derive user engagement through automatic personalization. Effective recommender systems may be among the tools that can help reverse the current decline of interest in local news sources. Our research explores approaches to learning localized models from user interaction data with news articles, particularly in news categories where there is intense local interest and there is a significant difference between users’ global and local news preferences. Specifically, we propose using such localized models in a session-based recommender system where the system can switch between users’ global and local preference models automatically when warranted. We report experiments performed on a news dataset from a local newspaper show that these local models, particularly the Life-and-Culture news category, do indeed provide more accuracy and effectiveness for personalization which, in turn, may lead to more user engagement with local news content.","PeriodicalId":162466,"journal":{"name":"Adjunct Proceedings of the 30th ACM Conference on User Modeling, Adaptation and Personalization","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117075933","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":"Monitoring the Learning Progress in Piano Playing with Hidden Markov Models","authors":"Nina Ziegenbein, J. Friedman, Alexandra Moringen","doi":"10.1145/3511047.3537666","DOIUrl":"https://doi.org/10.1145/3511047.3537666","url":null,"abstract":"Monitoring a learner’s performance during practice plays an important role in scaffolding. It helps with scheduling suitable practice exercises and, by doing so, sustains learner motivation and steady learning progress while moving through the curriculum. In this paper we present our approach for monitoring the learning progress of students learning to play piano with Hidden Markov Models. First, we present and implement the so-called practice modes, practice units that are derived from the original task by reducing its complexity and focusing on one or several relevant task dimensions. Second, for each practice mode, a Hidden Markov Model is trained to predict whether the player is in the Mastered or NonMastered latent state regarding the current task and practice mode.","PeriodicalId":162466,"journal":{"name":"Adjunct Proceedings of the 30th ACM Conference on User Modeling, Adaptation and Personalization","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130591401","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":"A complementary account to emotion extraction and classification in cultural heritage based on the Plutchik’s theory","authors":"Andrea Bolioli, A. Bosca, R. Damiano, Antonio Lieto, Manuel Striani","doi":"10.1145/3511047.3537659","DOIUrl":"https://doi.org/10.1145/3511047.3537659","url":null,"abstract":"The paper presents a combined approach to knowledge-based emotion attribution and classification of cultural items employed in the H2020 project SPICE. In particular, we show a preliminary experimentation conducted on a selection of items contributed by the GAM Museum in Turin (Galleria di Arte Moderna), pointing out how different language-based approaches to emotion categorization (used in the systems Sophia and DEGARI respectively) can be powerfully combined to cope with both coverage and extended affective attributions. Interestingly, both approaches are based on an ontology of the Plutchik’s theory of emotions.","PeriodicalId":162466,"journal":{"name":"Adjunct Proceedings of the 30th ACM Conference on User Modeling, Adaptation and Personalization","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126670314","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":"Understanding Privacy Decisions of Homeworkers During Video Conferences","authors":"E. Herder, Milan Gullit","doi":"10.1145/3511047.3537655","DOIUrl":"https://doi.org/10.1145/3511047.3537655","url":null,"abstract":"As a result of the COVID-19 pandemic, a lot of people have been forced to work from home. Particularly during video conferences, workers basically invite their colleagues, co-workers and supervisors into their homes, sacrificing portions of their privacy in the process. In this paper, we investigate which home-related and work-related factors are perceived as relevant for privacy. We asked participants to indicate their preferences for videoconferencing settings in various scenarios and also asked about their personal experiences. The results show that power distance plays a role, but that group size and familiarity with other group members are more decisive factors. We discuss implications of our findings in terms of user awareness and the benefits of different context-based default settings for videoconferencing privacy settings.","PeriodicalId":162466,"journal":{"name":"Adjunct Proceedings of the 30th ACM Conference on User Modeling, Adaptation and Personalization","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125289100","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":"Multi-agent Social Choice for Dynamic Fairness-aware Recommendation","authors":"R. Burke, Nicholas Mattei, Vladislav Grozin, A. Voida, Nasim Sonboli","doi":"10.1145/3511047.3538032","DOIUrl":"https://doi.org/10.1145/3511047.3538032","url":null,"abstract":"The pursuit of algorithmic fairness requires that we think differently about the idea of the “user” in personalized systems, such as recommender systems. The conventional definition of the user in such systems focuses on the receiver of recommendations, the individual to whom a particular personalization output is directed. Fairness, especially provider-side fairness, requires that we consider a broader array of system users and stakeholders, whose needs, interests and preferences may need to be modeled. In this paper, we describe a framework in which the interests of providers and other stakeholders are represented as agents. These agents participate in the production of recommendations through a two-stage social choice mechanism. This approach has the benefit of being able to represent a wide variety of fairness concepts and to extend to multiple fairness concerns.","PeriodicalId":162466,"journal":{"name":"Adjunct Proceedings of the 30th ACM Conference on User Modeling, Adaptation and Personalization","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125336983","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}