Kiemute Oyibo, I. Adaji, Rita Orji, Julita Vassileva
We are pleased to welcome you to the 1st International Workshop on Adaptive and Personalized Persuasive Technology (ADAPPT 2019). ADAPPT 2019 is a half-day workshop held in conjunction with the 27th ACM Conference on User Modeling, Adaptation and Personalization (UMAP 2018), 09-12 June 2019 in Larnaca, Spain. Persuasive technologies are increasingly being used to bring about behavior change in various domains of human endeavors, including health, education, commerce, energy conservation, safety, etc. However, research on personalizing and adapting them to their target users to make them more effective is still in its infancy. As such, for the first time, we proposed at the ACM UMAP 2019 conference the ADAPPT 2019 workshop. The workshop aims to bring together researchers and practitioners from academia and industry-working in the area of adapting and personalizing persuasive technologies-to present, discuss and share their work in progress with other members of the research community. Specifically, the workshop aims to provide a platform for stakeholders to brainstorm, identify and discuss the opportunities and challenges in the ADAPPT field as well as emerging techniques, methods and approaches to personalizing and adapting persuasive technologies to the target users. In the first edition of the workshop, we received 10 submissions from four different countries, including Canada, Germany, Nigeria and Spain, covering a wide range of topics in domains such as health, education, organization, social media, e-commerce, etc. Each of the 10 papers was reviewed by at least two reviewers, which included members of the organizing committee and external reviewers with expertise in different areas of persuasive technology research. All of the 10 papers, which include 4 full papers and 6 short papers, were accepted for presentation at the workshop.
{"title":"UMAP 2019 ADAPPT (Adaptive and Personalized Persuasive Technology) Workshop Chairs' Welcome & Organization","authors":"Kiemute Oyibo, I. Adaji, Rita Orji, Julita Vassileva","doi":"10.1145/3314183.3323848","DOIUrl":"https://doi.org/10.1145/3314183.3323848","url":null,"abstract":"We are pleased to welcome you to the 1st International Workshop on Adaptive and Personalized Persuasive Technology (ADAPPT 2019). ADAPPT 2019 is a half-day workshop held in conjunction with the 27th ACM Conference on User Modeling, Adaptation and Personalization (UMAP 2018), 09-12 June 2019 in Larnaca, Spain. Persuasive technologies are increasingly being used to bring about behavior change in various domains of human endeavors, including health, education, commerce, energy conservation, safety, etc. However, research on personalizing and adapting them to their target users to make them more effective is still in its infancy. As such, for the first time, we proposed at the ACM UMAP 2019 conference the ADAPPT 2019 workshop. The workshop aims to bring together researchers and practitioners from academia and industry-working in the area of adapting and personalizing persuasive technologies-to present, discuss and share their work in progress with other members of the research community. Specifically, the workshop aims to provide a platform for stakeholders to brainstorm, identify and discuss the opportunities and challenges in the ADAPPT field as well as emerging techniques, methods and approaches to personalizing and adapting persuasive technologies to the target users. In the first edition of the workshop, we received 10 submissions from four different countries, including Canada, Germany, Nigeria and Spain, covering a wide range of topics in domains such as health, education, organization, social media, e-commerce, etc. Each of the 10 papers was reviewed by at least two reviewers, which included members of the organizing committee and external reviewers with expertise in different areas of persuasive technology research. All of the 10 papers, which include 4 full papers and 6 short papers, were accepted for presentation at the workshop.","PeriodicalId":240482,"journal":{"name":"Adjunct Publication of the 27th Conference on User Modeling, Adaptation and Personalization","volume":"70 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":"129979845","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}
Menopause is a natural part of women's aging, but is often accompanied by an increased cardiometabolic risk (CMR), of which most women are unaware. Preventive self-care via mobile health applications (apps) is a promising way to address this issue, but research on apps for middle-aged women is limited. Further, modeling such risk is no trivial task in a non-clinical self-care context, where most biomarkers used in traditional models are unavailable. Machine learning (ML) is a potential option in this regard, but many ML approaches are effectively black box models, which leads to doubt regarding their trustworthiness. Therefore, in this paper we analyze and compare different decision tree and rule-based classification models, considered to be inherently interpretable, to assess the CMR of early middle-aged women in the context of a non-clinical self-care app. For this, we first defined a set of candidate determinants based on the feedback of potential users and domain experts. We then used data from a subset of the participants in the Study of Women's Health Across the Nation (SWAN) to compare these ML models with traditional risk score models, based on five cardiometabolic 10-year outcomes: heart attack, stroke, angina pectoris, diabetes, and metabolic syndrome.
{"title":"Classification of Cardiometabolic Risk in Early Middle-aged Women for Preventive Self-care Apps","authors":"Amaury Trujillo, Maria Claudia Buzzi","doi":"10.1145/3314183.3323677","DOIUrl":"https://doi.org/10.1145/3314183.3323677","url":null,"abstract":"Menopause is a natural part of women's aging, but is often accompanied by an increased cardiometabolic risk (CMR), of which most women are unaware. Preventive self-care via mobile health applications (apps) is a promising way to address this issue, but research on apps for middle-aged women is limited. Further, modeling such risk is no trivial task in a non-clinical self-care context, where most biomarkers used in traditional models are unavailable. Machine learning (ML) is a potential option in this regard, but many ML approaches are effectively black box models, which leads to doubt regarding their trustworthiness. Therefore, in this paper we analyze and compare different decision tree and rule-based classification models, considered to be inherently interpretable, to assess the CMR of early middle-aged women in the context of a non-clinical self-care app. For this, we first defined a set of candidate determinants based on the feedback of potential users and domain experts. We then used data from a subset of the participants in the Study of Women's Health Across the Nation (SWAN) to compare these ML models with traditional risk score models, based on five cardiometabolic 10-year outcomes: heart attack, stroke, angina pectoris, diabetes, and metabolic syndrome.","PeriodicalId":240482,"journal":{"name":"Adjunct Publication of the 27th Conference on User Modeling, Adaptation and Personalization","volume":"1 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":"129984936","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 reviews of apps are critically important in open mobile application markets, including the App Store and Google Play. Analyzing app reviews helps reveal any usability issues faced, desired improvements, and could also provide insights to guide future app designs. As a result, there is a growing demand for analysis of app reviews to enhance app usability, user experience, and hence improve overall app adoption. This is particularly true for apps targeting sensitive issues such as those promoting mental health. In this paper, we present the results of an analysis of 106 mental health app reviews from the App Store and Google Play. We mined and analyzed 1236 distinct reviews to identify usability issues. We classified app usability issues into six categories: bugs, poor user interface design, data loss, battery and memory usage issue, lack of guidance and explanation, and internet connectivity issue. The results could guide app designers on how to design apps especially those tailored to mental health to improve their usability.
{"title":"Usability Issues in Mental Health Applications","authors":"Felwah Alqahtani, Rita Orji","doi":"10.1145/3314183.3323676","DOIUrl":"https://doi.org/10.1145/3314183.3323676","url":null,"abstract":"User reviews of apps are critically important in open mobile application markets, including the App Store and Google Play. Analyzing app reviews helps reveal any usability issues faced, desired improvements, and could also provide insights to guide future app designs. As a result, there is a growing demand for analysis of app reviews to enhance app usability, user experience, and hence improve overall app adoption. This is particularly true for apps targeting sensitive issues such as those promoting mental health. In this paper, we present the results of an analysis of 106 mental health app reviews from the App Store and Google Play. We mined and analyzed 1236 distinct reviews to identify usability issues. We classified app usability issues into six categories: bugs, poor user interface design, data loss, battery and memory usage issue, lack of guidance and explanation, and internet connectivity issue. The results could guide app designers on how to design apps especially those tailored to mental health to improve their usability.","PeriodicalId":240482,"journal":{"name":"Adjunct Publication of the 27th Conference on User Modeling, Adaptation and Personalization","volume":"39 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":"128933484","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 world is changing and with the evolution of technology computers have become an essential part of humans' lives. Nowadays many people use computers for both work and personal life, and especially spending hours sitting at a desk in front of their computer screens. This phenomenon negatively influences people's health, affecting their skeletal and ocular systems. As a result, several different ergonomic solutions have been suggested to address these challenges. This paper proposes a solution which adjusts the computer screen position, elevation and orientation in order to reduce the physical load on the user and better fit it to their posture.
{"title":"Automatically Adjusting Computer Screen","authors":"Rotem Kronenberg, T. Kuflik","doi":"10.1145/3314183.3324980","DOIUrl":"https://doi.org/10.1145/3314183.3324980","url":null,"abstract":"The world is changing and with the evolution of technology computers have become an essential part of humans' lives. Nowadays many people use computers for both work and personal life, and especially spending hours sitting at a desk in front of their computer screens. This phenomenon negatively influences people's health, affecting their skeletal and ocular systems. As a result, several different ergonomic solutions have been suggested to address these challenges. This paper proposes a solution which adjusts the computer screen position, elevation and orientation in order to reduce the physical load on the user and better fit it to their posture.","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":"125436047","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}
A shared challenge in the domain of User Modeling, Adaptation and Personalisation is proposed for the 2019 EvalUMAP workshop whereby the evaluation of user models generating personalised push-notifications is to be explored. As such, this paper presents a description of the evaluation process, a solution to the first proposed challenge, a discussion of results obtained from the Gym-Push evaluation environment and a number of benchmarks which can be used as a baseline for future work.
{"title":"Generation and Evaluation of Personalised Push-Notifications","authors":"Kieran Fraser, Bilal Yousuf, Owen Conlan","doi":"10.1145/3314183.3323683","DOIUrl":"https://doi.org/10.1145/3314183.3323683","url":null,"abstract":"A shared challenge in the domain of User Modeling, Adaptation and Personalisation is proposed for the 2019 EvalUMAP workshop whereby the evaluation of user models generating personalised push-notifications is to be explored. As such, this paper presents a description of the evaluation process, a solution to the first proposed challenge, a discussion of results obtained from the Gym-Push evaluation environment and a number of benchmarks which can be used as a baseline for future work.","PeriodicalId":240482,"journal":{"name":"Adjunct Publication of the 27th Conference on User Modeling, Adaptation and Personalization","volume":"47 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":"121476910","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}
Matthias Schmidmaier, Zhiwei Han, Thomas Weber, Yuanting Liu, H. Hussmann
Integrated Development Environments (IDEs) are used for a varietyof software development tasks. Their complexity makes them chal-lenging to use though, especially for less experienced developers. In this paper, we outline our approach for an user-adaptive IDE that is able to track the interactions, recognize the user's intent and expertise, and provide relevant, personalized recommendations in real-time. To obtain a user model and provide recommendations, interaction data is processed in a two-stage process: first, we derive a bandit based global model of general task patterns from a dataset of labeled interactions. Second, when the user is working with the IDE, we apply a pre-trained classifier in real-time to get task labels from the user's interactions. With those and user feedback we fine-tune a local copy of the global model. As a result, we obtain a personalized user model which provides user-specific recommendations. We finally present various approaches for using these recommendations to adapt the IDE's interface. Modifications range from visual highlighting to task automation, including explanatory feedback.
{"title":"Real-Time Personalization in Adaptive IDEs","authors":"Matthias Schmidmaier, Zhiwei Han, Thomas Weber, Yuanting Liu, H. Hussmann","doi":"10.1145/3314183.3324975","DOIUrl":"https://doi.org/10.1145/3314183.3324975","url":null,"abstract":"Integrated Development Environments (IDEs) are used for a varietyof software development tasks. Their complexity makes them chal-lenging to use though, especially for less experienced developers. In this paper, we outline our approach for an user-adaptive IDE that is able to track the interactions, recognize the user's intent and expertise, and provide relevant, personalized recommendations in real-time. To obtain a user model and provide recommendations, interaction data is processed in a two-stage process: first, we derive a bandit based global model of general task patterns from a dataset of labeled interactions. Second, when the user is working with the IDE, we apply a pre-trained classifier in real-time to get task labels from the user's interactions. With those and user feedback we fine-tune a local copy of the global model. As a result, we obtain a personalized user model which provides user-specific recommendations. We finally present various approaches for using these recommendations to adapt the IDE's interface. Modifications range from visual highlighting to task automation, including explanatory feedback.","PeriodicalId":240482,"journal":{"name":"Adjunct Publication of the 27th Conference on User Modeling, Adaptation and Personalization","volume":"7 4","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120999598","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}
Customer Data Platform (CDP) is an integrated customer database operated by marketers. In the context of UMAP, this paper demonstrates a real-world CDP with a special focus on (1) simple and deterministic text-based behavioral profiling technique, and (2) GUI-based versatile tool for predictive analytics. Those functionalities are designed for those who have no expertise in machine learning and natural language processing, so the only thing marketers have to do is clicking some buttons on UI. Meanwhile, their back-end system ensures scalability and utility of the entire workflow from data collection and management to prediction and visualization.
客户数据平台(Customer Data Platform, CDP)是营销人员运营的综合性客户数据库。在UMAP的背景下，本文展示了一个真实的CDP，特别关注(1)简单和确定性的基于文本的行为分析技术，以及(2)基于gui的多功能预测分析工具。这些功能是为那些没有机器学习和自然语言处理专业知识的人设计的，所以营销人员唯一要做的就是点击UI上的一些按钮。同时，他们的后端系统确保了从数据收集和管理到预测和可视化的整个工作流程的可扩展性和实用性。
{"title":"Zero-Coding UMAP in Marketing: A Scalable Platform for Profiling and Predicting Customer Behavior by Just Clicking on the Screen","authors":"Takuya Kitazawa","doi":"10.1145/3314183.3324970","DOIUrl":"https://doi.org/10.1145/3314183.3324970","url":null,"abstract":"Customer Data Platform (CDP) is an integrated customer database operated by marketers. In the context of UMAP, this paper demonstrates a real-world CDP with a special focus on (1) simple and deterministic text-based behavioral profiling technique, and (2) GUI-based versatile tool for predictive analytics. Those functionalities are designed for those who have no expertise in machine learning and natural language processing, so the only thing marketers have to do is clicking some buttons on UI. Meanwhile, their back-end system ensures scalability and utility of the entire workflow from data collection and management to prediction and visualization.","PeriodicalId":240482,"journal":{"name":"Adjunct Publication of the 27th Conference on User Modeling, Adaptation and Personalization","volume":"15 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":"127757229","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}
Marios Belk, C. Fidas, E. Athanasopoulos, A. Pitsillides
It is our great pleasure to welcome you to the First International Workshop on Adaptive and Personalized Privacy and Security (APPS 2019). APPS 2019 (http://appsworkshop.cs.ucy.ac.cy) is a half-day workshop held on June 09, 2019, in conjunction with the ACM Conference on User Modeling, Adaptation and Personalization (ACM UMAP 2019) in Larnaca, Cyprus. Adaptive and personalized privacy and security aims at supporting privacy- and/or security-related tasks by leveraging on holistic user models which reflect the users' unique sociocultural, physical, physiological and technological context in which interaction takes place. As such, APPS 2019 aims to bring together researchers and practitioners working on diverse topics related to understanding and improving the usability of privacy and systems security, by applying user modeling, adaptation and personalization principles framed by User-Centered Design methods.
{"title":"Adaptive and Personalized Privacy and Security (APPS 2019): Workshop Chairs' Welcome and Organization","authors":"Marios Belk, C. Fidas, E. Athanasopoulos, A. Pitsillides","doi":"10.1145/3314183.3324963","DOIUrl":"https://doi.org/10.1145/3314183.3324963","url":null,"abstract":"It is our great pleasure to welcome you to the First International Workshop on Adaptive and Personalized Privacy and Security (APPS 2019). APPS 2019 (http://appsworkshop.cs.ucy.ac.cy) is a half-day workshop held on June 09, 2019, in conjunction with the ACM Conference on User Modeling, Adaptation and Personalization (ACM UMAP 2019) in Larnaca, Cyprus. Adaptive and personalized privacy and security aims at supporting privacy- and/or security-related tasks by leveraging on holistic user models which reflect the users' unique sociocultural, physical, physiological and technological context in which interaction takes place. As such, APPS 2019 aims to bring together researchers and practitioners working on diverse topics related to understanding and improving the usability of privacy and systems security, by applying user modeling, adaptation and personalization principles framed by User-Centered Design methods.","PeriodicalId":240482,"journal":{"name":"Adjunct Publication of the 27th Conference on User Modeling, Adaptation and Personalization","volume":"24 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":"134028009","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}
A. Wecker, Uri Schor, Dror Elovits, D. Ezra, T. Kuflik, Moshe Lavee, Vered Raziel-Kretzmer, Avigail Ohali, Lily Signoret
This paper briefly describes aspects of the Tikkoun Sofrim crowdsourcing webApp. Tikkoun Sofrim is a webApp which allows users to correct automatic transcriptions (AT) done by an AI Neural network engine. We look at the background of the crowdsourcing phenomenon in the use of automatic transcription of digital humanities documents. System structure is briefly described. We then examine personalization and adaption aspects at different stages of the user/application lifecycle Finally, we briefly list future challenges.
{"title":"Tikkoun Sofrim: A WebApp for Personalization and Adaptation of Crowdsourcing Transcriptions","authors":"A. Wecker, Uri Schor, Dror Elovits, D. Ezra, T. Kuflik, Moshe Lavee, Vered Raziel-Kretzmer, Avigail Ohali, Lily Signoret","doi":"10.1145/3314183.3324972","DOIUrl":"https://doi.org/10.1145/3314183.3324972","url":null,"abstract":"This paper briefly describes aspects of the Tikkoun Sofrim crowdsourcing webApp. Tikkoun Sofrim is a webApp which allows users to correct automatic transcriptions (AT) done by an AI Neural network engine. We look at the background of the crowdsourcing phenomenon in the use of automatic transcription of digital humanities documents. System structure is briefly described. We then examine personalization and adaption aspects at different stages of the user/application lifecycle Finally, we briefly list future challenges.","PeriodicalId":240482,"journal":{"name":"Adjunct Publication of the 27th Conference on User Modeling, Adaptation and Personalization","volume":"3 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":"115371833","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}
Erjon Skenderi, Ekaterina Olshannikova, Thomas Olsson, Jukka Huhtamäki, Sami Koivunen, Peng Yao, H. Huttunen
The increasing amount of data in social media enables new advanced user modeling approaches. This paper focuses on user profiling for diversity-enhancing recommender systems for finding new followees on Twitter. By combining social network analysis with Latent Dirichlet Allocation based content analysis, we defined three egocentric structural positions on the network extracted from Twitter data: Mentions of Mentions, Community Cluster, Dormant Ties (and the rest as a baseline condition). In addition to describing the data analysis procedure, we report preliminary empirical findings on a user-centered evaluation study of recommendations based on the proposed matching strategy and the presented structural positions. The investigation of the possible overlaps of the groups and the participants' evaluations of perceived relevance of the recommendation imply that the three positions are sufficiently mutually exclusive and thus could serve as new diversity-enhancing mechanisms in various people recommender systems.
{"title":"Investigation of Egocentric Social Structures for Diversity-Enhancing Followee Recommendations","authors":"Erjon Skenderi, Ekaterina Olshannikova, Thomas Olsson, Jukka Huhtamäki, Sami Koivunen, Peng Yao, H. Huttunen","doi":"10.1145/3314183.3323460","DOIUrl":"https://doi.org/10.1145/3314183.3323460","url":null,"abstract":"The increasing amount of data in social media enables new advanced user modeling approaches. This paper focuses on user profiling for diversity-enhancing recommender systems for finding new followees on Twitter. By combining social network analysis with Latent Dirichlet Allocation based content analysis, we defined three egocentric structural positions on the network extracted from Twitter data: Mentions of Mentions, Community Cluster, Dormant Ties (and the rest as a baseline condition). In addition to describing the data analysis procedure, we report preliminary empirical findings on a user-centered evaluation study of recommendations based on the proposed matching strategy and the presented structural positions. The investigation of the possible overlaps of the groups and the participants' evaluations of perceived relevance of the recommendation imply that the three positions are sufficiently mutually exclusive and thus could serve as new diversity-enhancing mechanisms in various people recommender systems.","PeriodicalId":240482,"journal":{"name":"Adjunct Publication of the 27th Conference on User Modeling, Adaptation and Personalization","volume":"123 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":"115783954","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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