Proceedings of the 27th ACM Conference on User Modeling, Adaptation and Personalization最新文献

Who Shares Fake News in Online Social Networks? 谁在社交网络上分享假新闻?

Proceedings of the 27th ACM Conference on User Modeling, Adaptation and Personalization Pub Date : 2019-06-07 DOI: 10.1145/3320435.3320456

Laura Burbach, Patrick Halbach, M. Ziefle, André Calero Valdez

引用次数: 16

Personalization of Persuasive Technology in Higher Education 高等教育中说服技术的个性化

Proceedings of the 27th ACM Conference on User Modeling, Adaptation and Personalization Pub Date : 2019-06-07 DOI: 10.1145/3320435.3320478

F. Orji, Kiemute Oyibo, Rita Orji, J. Greer, Julita Vassileva

{"title":"Personalization of Persuasive Technology in Higher Education","authors":"F. Orji, Kiemute Oyibo, Rita Orji, J. Greer, Julita Vassileva","doi":"10.1145/3320435.3320478","DOIUrl":"https://doi.org/10.1145/3320435.3320478","url":null,"abstract":"The success of persuasive systems in changing people's attitudes and behaviours has been established in various domains. Specifically, research has shown that personalized persuasive technology is more effective at achieving the desired goal than the one-size-fits-all approach. However, in the education domain, there are limited studies on the personalization of persuasive strategies to students. To advance persuasive technology research in this area, we investigated the susceptibility of undergraduate students (n = 243) to four persuasive strategies (Reward, Competition, Social Comparison and Social Learning) in order to provide a guideline for designing and personalizing persuasive systems in education. These four strategies were chosen because research on persuasion has established their effectiveness in changing behaviour and/or attitude. The results of our analysis reveal that students are more susceptible to Reward, followed by Competition and Social Comparison (both of which come in the second place) and Social Learning (the least persuasive). Moreover, there is no gender difference in the persuasiveness of the strategies. Therefore, in choosing persuasive strategies to motivate student's learning and success, among the strategies we investigated, Reward should be given priority, followed by Competition and Social Comparison, while Social Learning should be least favoured.","PeriodicalId":254537,"journal":{"name":"Proceedings of the 27th ACM Conference on User Modeling, Adaptation and Personalization","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116132946","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}

引用次数: 19

Designing Culturally-appropriate Persuasive Technology to Promote Positive Work Attitudes among Workers in Public Workplaces 设计与文化相适应的说服技术以促进公共工作场所工人的积极工作态度

Proceedings of the 27th ACM Conference on User Modeling, Adaptation and Personalization Pub Date : 2019-06-07 DOI: 10.1145/3320435.3323465

M. Nkwo

引用次数: 11

Value Driven Representation for Human-in-the-Loop Reinforcement Learning 人在环强化学习的价值驱动表示

Proceedings of the 27th ACM Conference on User Modeling, Adaptation and Personalization Pub Date : 2019-06-07 DOI: 10.1145/3320435.3320471

Ramtin Keramati, E. Brunskill

引用次数: 3

"An Unscented Hound for Working Memory" and the Cognitive Adaptation of User Interfaces “工作记忆的无气味猎犬”和用户界面的认知适应

Proceedings of the 27th ACM Conference on User Modeling, Adaptation and Personalization Pub Date : 2019-06-07 DOI: 10.1145/3320435.3320443

B. Sguerra, P. Jouvelot

{"title":"\"An Unscented Hound for Working Memory\" and the Cognitive Adaptation of User Interfaces","authors":"B. Sguerra, P. Jouvelot","doi":"10.1145/3320435.3320443","DOIUrl":"https://doi.org/10.1145/3320435.3320443","url":null,"abstract":"An Unscented Hound for Working Memory (AUHWM) is a new framework for the real-time tracking of human Working Memory (WM) that can be used to adapt computer interfaces to users' available cognitive resources. WM is the part of human cognition responsible for the short term storing and handling of information; it can, in stressful situations, under information overload or when suffering from dementia-like diseases, become severely limited, possibly leading to poor decision making. Our preliminary results suggest that AUHWM can provide a precise and timely assessment of WM capacity, so that the cognitive load a specific task imposes on users can be adapted, e.g., at the User Interface (UI) level. AUHWM is based on a low-level stochastic discrete model of human WM dynamics, implemented as a Gradient-Boosting-derived deterministic algorithm that simulates users' oblivion. AUHWM also performs Unscented Kalman filtering to track users' WM-specific parameters in real time, thus providing a dynamic assessment of their cognitive resources. Our approach has been tested and validated using data collected from Match$ ^2$s, a visual memory game played by 18 users in another study. Going beyond real-time WM tracking, AUHWM is intended to also be used for WM prediction, paving the way to the adaptation of tasks and their UIs in real time as a function of users' cognitive abilities; we detail an example of such an adapted system, and provide experimental evidence this approach could lead to future enhanced WM-adapted UIs.","PeriodicalId":254537,"journal":{"name":"Proceedings of the 27th ACM Conference on User Modeling, Adaptation and Personalization","volume":"206 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124639275","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}

引用次数: 3

Engagement, Metrics and Personalisation: the Good, the Bad and the Ugly 用户粘性，参数和个性化:好，坏和丑

Proceedings of the 27th ACM Conference on User Modeling, Adaptation and Personalization Pub Date : 2019-06-07 DOI: 10.1145/3320435.3323709

M. Lalmas

{"title":"Engagement, Metrics and Personalisation: the Good, the Bad and the Ugly","authors":"M. Lalmas","doi":"10.1145/3320435.3323709","DOIUrl":"https://doi.org/10.1145/3320435.3323709","url":null,"abstract":"User engagement plays a central role in companies and organisations operating online services. A main challenge is to leverage knowledge about the online interaction of users to understand what engage them short-term and more importantly long-term. Two critical steps of improving user engagement are defining the right metrics and properly optimising for them. A common way that engagement is measured and understood is through the definition and development of metrics of user satisfaction, which can act as proxy of short-term user engagement, mostly at session level. In the context of recommender systems, developing a better understanding of how users interact (implicit signals) with them during their online session is important for developing metrics of user satisfaction. Detecting and understanding implicit signals of user satisfaction are essential for enhancing the quality of the recommendations. When users interact with the recommendations served to them, they leave behind fine-grained traces of interaction patterns, which can be leveraged to predict how satisfying their experience was. This talk will present various works and personal thoughts on how to measure user engagement. It will discuss the definition and development of metrics of user satisfaction that can be used as proxy of user engagement, and will include cases of good, bad and ugly scenarios. An important message will be to show that, when aiming to personalise the recommendations, it is important to consider the heterogeneity of both user and content to formalise the notion of satisfaction, and in turn design the appropriate satisfaction metrics to capture these.","PeriodicalId":254537,"journal":{"name":"Proceedings of the 27th ACM Conference on User Modeling, Adaptation and Personalization","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114307815","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}

引用次数: 0

Relationship between Device Performance, Trust and User Behaviour in a Care-taking Scenario 看护场景中设备性能、信任和用户行为的关系

Proceedings of the 27th ACM Conference on User Modeling, Adaptation and Personalization Pub Date : 2019-06-07 DOI: 10.1145/3320435.3320440

Andisheh Partovi, Ingrid Zukerman, Kai Zhan, Nora Hamacher, J. Hohwy

引用次数: 4

Exploring the Potential of the Resolving Sets Model for Introducing Serendipity to Recommender Systems 探索解决集模型在推荐系统中引入偶然性的潜力

Proceedings of the 27th ACM Conference on User Modeling, Adaptation and Personalization Pub Date : 2019-06-07 DOI: 10.1145/3320435.3323467

Noa Tuval

{"title":"Exploring the Potential of the Resolving Sets Model for Introducing Serendipity to Recommender Systems","authors":"Noa Tuval","doi":"10.1145/3320435.3323467","DOIUrl":"https://doi.org/10.1145/3320435.3323467","url":null,"abstract":"Recommender systems offer recommendations based on user's previous ratings. However, sometimes the user is interested in unusual and interesting items that do not exactly match her user profile, as defined by the system. Serendipity, a concept that can be interpreted primarily as surprise, is one of the \"beyond-accuracy\" aspects that have been proposed to be considered to meet user's expectations for the recommendations she/he gets. Although recent studies attempt to address the serendipity problem, there is still a variety of interpretations regarding the definition, the measurement and the application of serendipity in recommender systems. Our proposed method follows the distance-based approach for multi-dimensional serendipity measurement, which refers to the expected items for the user as a benchmark for measuring serendipity. For integrating serendipity into recommendations, we propose a novel serendipity-oriented user modeling method, based on graph-theory approach - resolving sets in a graph, which enables finding serendipitous items in a multi-dimensional content-based space by detecting the expected items for the user.","PeriodicalId":254537,"journal":{"name":"Proceedings of the 27th ACM Conference on User Modeling, Adaptation and Personalization","volume":"66 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124638834","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}

引用次数: 1

Extending a Tag-based Collaborative Recommender with Co-occurring Information Interests 扩展一个基于标签的协同推荐系统

Proceedings of the 27th ACM Conference on User Modeling, Adaptation and Personalization Pub Date : 2019-06-07 DOI: 10.1145/3320435.3320458

Noemi Mauro, L. Ardissono

{"title":"Extending a Tag-based Collaborative Recommender with Co-occurring Information Interests","authors":"Noemi Mauro, L. Ardissono","doi":"10.1145/3320435.3320458","DOIUrl":"https://doi.org/10.1145/3320435.3320458","url":null,"abstract":"Collaborative Filtering is largely applied to personalize item recommendation but its performance is affected by the sparsity of rating data. In order to address this issue, recent systems have been developed to improve recommendation by extracting latent factors from the rating matrices, or by exploiting trust relations established among users in social networks. In this work, we are interested in evaluating whether other sources of preference information than ratings and social ties can be used to improve recommendation performance. Specifically, we aim at testing whether the integration of frequently co-occurring interests in information search logs can improve recommendation performance in User-to-User Collaborative Filtering (U2UCF). For this purpose, we propose the Extended Category-based Collaborative Filtering (ECCF) recommender, which enriches category-based user profiles derived from the analysis of rating behavior with data categories that are frequently searched together by people in search sessions. We test our model using a big rating dataset and a log of a largely used search engine to extract the co-occurrence of interests. The experiments show that ECCF outperforms U2UCF and category-based collaborative recommendation in accuracy, MRR, diversity of recommendations and user coverage. Moreover, it outperforms the SVD++ Matrix Factorization algorithm in accuracy and diversity of recommendation lists.","PeriodicalId":254537,"journal":{"name":"Proceedings of the 27th ACM Conference on User Modeling, Adaptation and Personalization","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127739729","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}

引用次数: 2

Towards Social Choice-based Explanations in Group Recommender Systems 群体推荐系统中基于社会选择的解释

Proceedings of the 27th ACM Conference on User Modeling, Adaptation and Personalization Pub Date : 2019-06-07 DOI: 10.1145/3320435.3320437

Thi Ngoc Trang Tran, Müslüm Atas, A. Felfernig, Viet-Man Le, Ralph Samer, Martin Stettinger

{"title":"Towards Social Choice-based Explanations in Group Recommender Systems","authors":"Thi Ngoc Trang Tran, Müslüm Atas, A. Felfernig, Viet-Man Le, Ralph Samer, Martin Stettinger","doi":"10.1145/3320435.3320437","DOIUrl":"https://doi.org/10.1145/3320435.3320437","url":null,"abstract":"Explanations help users to better understand why a set of items has been recommended. Compared to single user recommender systems, explanations in group recommender systems have further goals. Examples thereof are fairness which helps to take into account as much as possible group members' preferences and consensus which persuades group members to agree on a decision. This paper proposes different explanation types and investigates which explanation best helps to increase the fairness perception, consensus perception, and satisfaction of group members with regard to group recommendations. We conducted a user study to evaluate the proposed explanations. The results show that explanations which take into account preferences of all or the majority of group members achieve the best results in terms of the mentioned aspects. Moreover, there exist positive correlations among these aspects, i.e., as the perceived fairness (or the perceived consensus) of explanations increases, so does the satisfaction of users with regard to group recommendations. In addition, in the context of repeated decisions, the inclusion of group members' satisfaction from previous decisions in the explanations helps to improve the fairness perception of users with regard to group recommendations.","PeriodicalId":254537,"journal":{"name":"Proceedings of the 27th ACM Conference on User Modeling, Adaptation and Personalization","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114465388","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}

引用次数: 27