Many explainable recommender systems construct explanations of the recommendations these models produce, but it continues to be a di cult problem to explain to a user why an item was recommended by these high-dimensional latent factor models. In this work, We propose a technique that joint interpretations into recommendation training to make accurate predictions while at the same time learning to produce recommendations which have the most explanatory utility to the user. Our evaluation shows that we can jointly learn to make accurate and meaningful explanations with only a small sacri ce in recommendation accuracy. We also develop a new algorithm to measure explanation delity for the interpretation of top-n rankings. We prove that our approach can form the basis of a universal approach to explanation generation in recommender systems.
{"title":"NEAR: A Partner to Explain Any Factorised Recommender System","authors":"Sixun Ouyang, A. Lawlor","doi":"10.1145/3314183.3323457","DOIUrl":"https://doi.org/10.1145/3314183.3323457","url":null,"abstract":"Many explainable recommender systems construct explanations of the recommendations these models produce, but it continues to be a di cult problem to explain to a user why an item was recommended by these high-dimensional latent factor models. In this work, We propose a technique that joint interpretations into recommendation training to make accurate predictions while at the same time learning to produce recommendations which have the most explanatory utility to the user. Our evaluation shows that we can jointly learn to make accurate and meaningful explanations with only a small sacri ce in recommendation accuracy. We also develop a new algorithm to measure explanation delity for the interpretation of top-n rankings. We prove that our approach can form the basis of a universal approach to explanation generation in recommender systems.","PeriodicalId":240482,"journal":{"name":"Adjunct Publication of the 27th Conference on User Modeling, Adaptation and Personalization","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115055842","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}
Kiemute Oyibo, Abdul-Hammid Olagunju, Babatunde Olabenjo, I. Adaji, R. Deters, Julita Vassileva
Physical inactivity has been recognized as one of the leading causes of non-communicable diseases and mortality globally. Though persuasive technology has been identified as a potential tool for tackling physical inactivity and sedentary behaviors, very little attention has been paid to investigating the effectiveness of culture-tailored interventions in the wild. To bridge this gap, we designed and implemented two versions of a fitness app we called BEN'FIT [personal version (PV) and social version (SV)] targeted at encouraging regular bodyweight exercise behavior on the home front. The PV and SV versions are targeted at users from individualist and collectivist cultures, respectively. In this paper, we describe the empirical findings that informed the design and implementation of both versions of the BEN'FIT app, their features and how we intend to evaluate them in a pilot field study among our target audience once we complete the implementation of the app.
{"title":"BEN'FIT: Design, Implementation and Evaluation of a Culture-Tailored Fitness App","authors":"Kiemute Oyibo, Abdul-Hammid Olagunju, Babatunde Olabenjo, I. Adaji, R. Deters, Julita Vassileva","doi":"10.1145/3314183.3323854","DOIUrl":"https://doi.org/10.1145/3314183.3323854","url":null,"abstract":"Physical inactivity has been recognized as one of the leading causes of non-communicable diseases and mortality globally. Though persuasive technology has been identified as a potential tool for tackling physical inactivity and sedentary behaviors, very little attention has been paid to investigating the effectiveness of culture-tailored interventions in the wild. To bridge this gap, we designed and implemented two versions of a fitness app we called BEN'FIT [personal version (PV) and social version (SV)] targeted at encouraging regular bodyweight exercise behavior on the home front. The PV and SV versions are targeted at users from individualist and collectivist cultures, respectively. In this paper, we describe the empirical findings that informed the design and implementation of both versions of the BEN'FIT app, their features and how we intend to evaluate them in a pilot field study among our target audience once we complete the implementation of the app.","PeriodicalId":240482,"journal":{"name":"Adjunct Publication of the 27th Conference on User Modeling, Adaptation and Personalization","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125585427","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}
User modeling has become an indispensable feature of a plethora of different digital services such as search engines, social media or e-commerce. Indeed, decision procedures of online algorithmic systems apply various methods including machine learning (ML) to generate virtual models of billions of human beings based on large amounts of personal and other data. Recently, there has been a call for a "Right to Reasonable Inferences" for Europe's General Data Protection Regulation (GDPR). Here, we explore a conceptualization of reasonable inference in the context of image analytics that refers to the notion of evidence in theoretical reasoning. The main goal of this paper is to start defining principles for reasonable image inferences, in particular, portraits of individuals. Based on an image analytics case study, we use the notions of first- and second-order inferences to determine the reasonableness of predicted concepts. Finally, we highlight three key challenges for the future of this research space: first, we argue for the potential value of hidden quasi-semantics. Second, we indicate that automatic inferences can create a fundamental trade-off between privacy preservation and "model fit" and, third, we end with the question whether human reasoning can serve as a normative benchmark for reasonable automatic inferences.
{"title":"Setting the Stage: Towards Principles for Reasonable Image Inferences","authors":"Severin Engelmann, Jens Grossklags","doi":"10.1145/3314183.3323846","DOIUrl":"https://doi.org/10.1145/3314183.3323846","url":null,"abstract":"User modeling has become an indispensable feature of a plethora of different digital services such as search engines, social media or e-commerce. Indeed, decision procedures of online algorithmic systems apply various methods including machine learning (ML) to generate virtual models of billions of human beings based on large amounts of personal and other data. Recently, there has been a call for a \"Right to Reasonable Inferences\" for Europe's General Data Protection Regulation (GDPR). Here, we explore a conceptualization of reasonable inference in the context of image analytics that refers to the notion of evidence in theoretical reasoning. The main goal of this paper is to start defining principles for reasonable image inferences, in particular, portraits of individuals. Based on an image analytics case study, we use the notions of first- and second-order inferences to determine the reasonableness of predicted concepts. Finally, we highlight three key challenges for the future of this research space: first, we argue for the potential value of hidden quasi-semantics. Second, we indicate that automatic inferences can create a fundamental trade-off between privacy preservation and \"model fit\" and, third, we end with the question whether human reasoning can serve as a normative benchmark for reasonable automatic inferences.","PeriodicalId":240482,"journal":{"name":"Adjunct Publication of the 27th Conference on User Modeling, Adaptation and Personalization","volume":"54 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121777417","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}
Social Network Sites (SNSs) like Facebook or Instagram are spaces where people expose their lives to wide and diverse audiences. This practice can lead to unwanted incidents such as reputation damage, job loss or harassment when pieces of private information reach unintended recipients. As a consequence, users often regret to have posted private information in these platforms and proceed to delete such content after having a negative experience. Risk awareness is a strategy that can be used to persuade users towards safer privacy decisions. However, many risk awareness technologies for SNSs assume that information about risks is retrieved and measured by an expert in the field. Consequently, risk estimation is an activity that is often passed over despite its importance. In this work we introduce an approach that employs deleted posts as risk information vehicles to measure the frequency and consequence level of self-disclosure patterns in SNSs. In this method, consequence is reported by the users through an ordinal scale and used later on to compute a risk criticality index. We thereupon show how this index can serve in the design of adaptive privacy nudges for SNSs.
{"title":"Learning from Online Regrets: From Deleted Posts to Risk Awareness in Social Network Sites","authors":"N. E. D. Ferreyra, Rene Meis, M. Heisel","doi":"10.1145/3314183.3323849","DOIUrl":"https://doi.org/10.1145/3314183.3323849","url":null,"abstract":"Social Network Sites (SNSs) like Facebook or Instagram are spaces where people expose their lives to wide and diverse audiences. This practice can lead to unwanted incidents such as reputation damage, job loss or harassment when pieces of private information reach unintended recipients. As a consequence, users often regret to have posted private information in these platforms and proceed to delete such content after having a negative experience. Risk awareness is a strategy that can be used to persuade users towards safer privacy decisions. However, many risk awareness technologies for SNSs assume that information about risks is retrieved and measured by an expert in the field. Consequently, risk estimation is an activity that is often passed over despite its importance. In this work we introduce an approach that employs deleted posts as risk information vehicles to measure the frequency and consequence level of self-disclosure patterns in SNSs. In this method, consequence is reported by the users through an ordinal scale and used later on to compute a risk criticality index. We thereupon show how this index can serve in the design of adaptive privacy nudges for SNSs.","PeriodicalId":240482,"journal":{"name":"Adjunct Publication of the 27th Conference on User Modeling, Adaptation and Personalization","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126881902","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 heterogeneity of the audience of cultural heritage (CH) institutions introduces numerous challenges to the delivery of meaningful CH content. People with diverse cognitive characteristics are engaged with varying CH activities (e.g., games, guided visits) and considering that cognitive characteristics define the way we process information, our experience, behavior, and knowledge acquisition are influenced. Our recent studies provide evidence that human cognition should be considered as an important personalization factor within CH contexts, and thus, we developed a framework that delivers cognition-centered personalized CH activities. The efficiency and the efficacy of the framework have been successfully assessed, but, non-technical users may face difficulties when attempting to use it and create personalized CH activities. In this paper, we present DeCACHe which supports CH designers in developing cognition-centered personalized CH activities throughout different aspects of the design lifecycle. We also report a case study with one CH designer, who used our tool to produce two versions of his CH game for people with different cognitive characteristics.
{"title":"Supporting Designers in Creating Cognition-centered Personalized Cultural Heritage Activities","authors":"G. Raptis, N. Avouris","doi":"10.1145/3314183.3323868","DOIUrl":"https://doi.org/10.1145/3314183.3323868","url":null,"abstract":"The heterogeneity of the audience of cultural heritage (CH) institutions introduces numerous challenges to the delivery of meaningful CH content. People with diverse cognitive characteristics are engaged with varying CH activities (e.g., games, guided visits) and considering that cognitive characteristics define the way we process information, our experience, behavior, and knowledge acquisition are influenced. Our recent studies provide evidence that human cognition should be considered as an important personalization factor within CH contexts, and thus, we developed a framework that delivers cognition-centered personalized CH activities. The efficiency and the efficacy of the framework have been successfully assessed, but, non-technical users may face difficulties when attempting to use it and create personalized CH activities. In this paper, we present DeCACHe which supports CH designers in developing cognition-centered personalized CH activities throughout different aspects of the design lifecycle. We also report a case study with one CH designer, who used our tool to produce two versions of his CH game for people with different cognitive characteristics.","PeriodicalId":240482,"journal":{"name":"Adjunct Publication of the 27th Conference on User Modeling, Adaptation and Personalization","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130383303","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, M. Degemmis, G. Semeraro
User profiling is becoming increasingly holistic by including aspects of the user that until a few years ago seemed irrelevant. The content that users produce on the Internet and social networks is an essential source of information about their habits, preferences, and behaviors in many situations. One factor that has proved to be very important for obtaining a complete user profile that includes her psychological traits are the emotions experienced. Therefore, it is of great interest to the research community to develop approaches for identifying emotions from the text that are accurate and robust in situations of everyday writing. In this work, we propose a classification approach based on deep neural networks, Bi-LSTM, CNN, and self-attention demonstrating its effectiveness on different datasets. Moreover, we compare three pre-trained word-embeddings for words encoding. The encouraging results obtained on state-of-the-art datasets allow us to confirm the validity of the model and to discuss what are the best word embeddings to adopt for the task of emotion detection. As a consequence of the great importance of deep learning in the research community, we promote our model as a starting point for further investigations in the domain.
{"title":"A Comparison of Word-Embeddings in Emotion Detection from Text using BiLSTM, CNN and Self-Attention","authors":"Marco Polignano, Pierpaolo Basile, M. Degemmis, G. Semeraro","doi":"10.1145/3314183.3324983","DOIUrl":"https://doi.org/10.1145/3314183.3324983","url":null,"abstract":"User profiling is becoming increasingly holistic by including aspects of the user that until a few years ago seemed irrelevant. The content that users produce on the Internet and social networks is an essential source of information about their habits, preferences, and behaviors in many situations. One factor that has proved to be very important for obtaining a complete user profile that includes her psychological traits are the emotions experienced. Therefore, it is of great interest to the research community to develop approaches for identifying emotions from the text that are accurate and robust in situations of everyday writing. In this work, we propose a classification approach based on deep neural networks, Bi-LSTM, CNN, and self-attention demonstrating its effectiveness on different datasets. Moreover, we compare three pre-trained word-embeddings for words encoding. The encouraging results obtained on state-of-the-art datasets allow us to confirm the validity of the model and to discuss what are the best word embeddings to adopt for the task of emotion detection. As a consequence of the great importance of deep learning in the research community, we promote our model as a starting point for further investigations in the domain.","PeriodicalId":240482,"journal":{"name":"Adjunct Publication of the 27th Conference on User Modeling, Adaptation and Personalization","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133505512","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}
Adaptive systems are based on user preferences and needs, while sensing and context awareness are also essential features. Recently, the notion of smart homes requires the collection of different data from users, in order to provide them with a personalized user experience. In this context, smart water management can facilitate activities within the smart home. User privacy protection is vital in this environment to provide adaptive data collection and usage. In this paper, we introduce our vision towards user privacy protection in this setting by specifying and considering user privacy preferences, and we present our ongoing work and its initial results. The current work is being conducted in the framework of the TAMIT research project. This initial work will serve as a basis for the integration of user privacy management within TAMIT and can be a useful source of information for the management of user privacy preferences in similar platforms.
{"title":"Towards Considering User Privacy Preferences in Smart Water Management","authors":"A. Kounoudes, G. Kapitsaki, M. Milis","doi":"10.1145/3314183.3324968","DOIUrl":"https://doi.org/10.1145/3314183.3324968","url":null,"abstract":"Adaptive systems are based on user preferences and needs, while sensing and context awareness are also essential features. Recently, the notion of smart homes requires the collection of different data from users, in order to provide them with a personalized user experience. In this context, smart water management can facilitate activities within the smart home. User privacy protection is vital in this environment to provide adaptive data collection and usage. In this paper, we introduce our vision towards user privacy protection in this setting by specifying and considering user privacy preferences, and we present our ongoing work and its initial results. The current work is being conducted in the framework of the TAMIT research project. This initial work will serve as a basis for the integration of user privacy management within TAMIT and can be a useful source of information for the management of user privacy preferences in similar platforms.","PeriodicalId":240482,"journal":{"name":"Adjunct Publication of the 27th Conference on User Modeling, Adaptation and Personalization","volume":"72 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132526922","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}
L. Ardissono, Cristina Gena, T. Kuflik, Noemi Mauro
It is our great pleasure to welcome you to the ACM 2019 PATCH. Following the successful series of PATCH workshops, started in 2007, PATCH 2019 is organized as the meeting point between state of the art cultural heritage research and personalization - using any kind of technology, while focusing on ubiquitous and adaptive scenarios, to enhance the personal experience in cultural heritage sites. The workshop is aimed at bringing together researchers and practitioners who are working on various aspects of cultural heritage and are interested in exploring the potential of state of the art of personalized approaches that may enhance the CH visit experience.
{"title":"UMAP PATCH 2019 Chairs' Welcome","authors":"L. Ardissono, Cristina Gena, T. Kuflik, Noemi Mauro","doi":"10.1145/3314183.3323860","DOIUrl":"https://doi.org/10.1145/3314183.3323860","url":null,"abstract":"It is our great pleasure to welcome you to the ACM 2019 PATCH. Following the successful series of PATCH workshops, started in 2007, PATCH 2019 is organized as the meeting point between state of the art cultural heritage research and personalization - using any kind of technology, while focusing on ubiquitous and adaptive scenarios, to enhance the personal experience in cultural heritage sites. The workshop is aimed at bringing together researchers and practitioners who are working on various aspects of cultural heritage and are interested in exploring the potential of state of the art of personalized approaches that may enhance the CH visit experience.","PeriodicalId":240482,"journal":{"name":"Adjunct Publication of the 27th Conference on User Modeling, Adaptation and Personalization","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132019836","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}
F. Fontanella, M. Molinara, Arturo Gallozzi, M. Cigola, L. Senatore, R. Florio, P. Clini, F. C. D'Amico
In this digital era, one of the main challenge faced by cultural heritage is digitization. This challenge is particularly hard in countries like Italy, characterized by an extremely high number of Cultural goods. Data acquisition for many of these Cultural Heritage is extremely difficult, because of the complexity of surveys through traditional methodologies. In this paper, we propose a novel approach to the knowledge and data acquisition Cultural Heritage based on social media. The proposed approach, named "HeritageGo" (HeGo), transforms the user as an actor of the procedures for the acquisition of raw data. The paper also describes the first experiments focusing on the metric quality of the models obtained with SfM methodologies from raw data acquired by users.
{"title":"HeritageGO (HeGO): A Social Media Based Project for Cultural Heritage Valorization","authors":"F. Fontanella, M. Molinara, Arturo Gallozzi, M. Cigola, L. Senatore, R. Florio, P. Clini, F. C. D'Amico","doi":"10.1145/3314183.3323863","DOIUrl":"https://doi.org/10.1145/3314183.3323863","url":null,"abstract":"In this digital era, one of the main challenge faced by cultural heritage is digitization. This challenge is particularly hard in countries like Italy, characterized by an extremely high number of Cultural goods. Data acquisition for many of these Cultural Heritage is extremely difficult, because of the complexity of surveys through traditional methodologies. In this paper, we propose a novel approach to the knowledge and data acquisition Cultural Heritage based on social media. The proposed approach, named \"HeritageGo\" (HeGo), transforms the user as an actor of the procedures for the acquisition of raw data. The paper also describes the first experiments focusing on the metric quality of the models obtained with SfM methodologies from raw data acquired by users.","PeriodicalId":240482,"journal":{"name":"Adjunct Publication of the 27th Conference on User Modeling, Adaptation and Personalization","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132097734","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}
ACM UMAP - User Modelling, Adaptation and Personalization is the premier international conference for researchers and practitioners working on systems that adapt to individual users, to groups of users, and that collect, represent, and model user information. The Theory, Opinion and Reflection (TOR) track at UMAP is designed to highlight emerging areas of inquiry in UMAP and to promote discussion of potentially visionary ideas.
{"title":"UMAP 2019 Theory, Reflection, and Opinion Track: Chairs' Welcome and Overview","authors":"G. Houben, B. Mobasher","doi":"10.1145/3314183.3326603","DOIUrl":"https://doi.org/10.1145/3314183.3326603","url":null,"abstract":"ACM UMAP - User Modelling, Adaptation and Personalization is the premier international conference for researchers and practitioners working on systems that adapt to individual users, to groups of users, and that collect, represent, and model user information. The Theory, Opinion and Reflection (TOR) track at UMAP is designed to highlight emerging areas of inquiry in UMAP and to promote discussion of potentially visionary ideas.","PeriodicalId":240482,"journal":{"name":"Adjunct Publication of the 27th Conference on User Modeling, Adaptation and Personalization","volume":"145 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122622208","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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