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Proceedings of the 2016 Conference on User Modeling Adaptation and Personalization最新文献

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Eliciting Users' Attitudes toward Smart Devices 激发用户对智能设备的态度
Proceedings of the 2016 Conference on User Modeling Adaptation and Personalization Pub Date : 2016-07-13 DOI: 10.1145/2930238.2930241
Kai Zhan, Ingrid Zukerman, Masud Moshtaghi, G. Rees
{"title":"Eliciting Users' Attitudes toward Smart Devices","authors":"Kai Zhan, Ingrid Zukerman, Masud Moshtaghi, G. Rees","doi":"10.1145/2930238.2930241","DOIUrl":"https://doi.org/10.1145/2930238.2930241","url":null,"abstract":"This paper presents a study to determine users' attitudes toward smart devices. We conducted a web survey to elicit users' ratings for devices and combinations of tasks and devices; the results of this survey led to the development of a Recommender System (RS) for smart devices for particular tasks. We investigated user- and item-based Collaborative Filters, and compared their performance with that of global and demographic RS baselines. We then developed a technique based on Principal Components Analysis to select a subset of the original survey questions that supports the prediction of users' ratings for device-task combinations. Our results show that the accuracy of an RS that asks only a small subset of the survey questions is similar to that of an RS that predicts users' answers to one survey question on the basis of their answers to all the other questions.","PeriodicalId":339100,"journal":{"name":"Proceedings of the 2016 Conference on User Modeling Adaptation and Personalization","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122198058","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: 5
Analyzing Aggregated Semantics-enabled User Modeling on Google+ and Twitter for Personalized Link Recommendations 在Google+和Twitter上分析聚合语义支持的用户建模，以进行个性化链接推荐
Proceedings of the 2016 Conference on User Modeling Adaptation and Personalization Pub Date : 2016-07-13 DOI: 10.1145/2930238.2930278
Guangyuan Piao, J. Breslin
{"title":"Analyzing Aggregated Semantics-enabled User Modeling on Google+ and Twitter for Personalized Link Recommendations","authors":"Guangyuan Piao, J. Breslin","doi":"10.1145/2930238.2930278","DOIUrl":"https://doi.org/10.1145/2930238.2930278","url":null,"abstract":"In this paper, we study if reusing Google+ profiles can provide reliable recommendations on Twitter to resolve the cold start problem. Next, we investigate the impact of giving different weights for aggregating user profiles from two OSNs and present that giving a higher weight to the targeted OSN profile for aggregation allows the best performance in the context of a personalized link recommender system. Finally, we propose a user modeling strategy which combines entity-and category-based user profiles using with a discounting strategy. Results show that our proposed strategy improves the quality of user modeling significantly compared to the baseline method.","PeriodicalId":339100,"journal":{"name":"Proceedings of the 2016 Conference on User Modeling Adaptation and Personalization","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129874569","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: 23
An Experimental Study in Cross-Representation Mediation of User Models 用户模型交叉表征中介的实验研究
Proceedings of the 2016 Conference on User Modeling Adaptation and Personalization Pub Date : 2016-07-13 DOI: 10.1145/2930238.2930263
F. Cena, Cristina Gena, Claudia Picardi
{"title":"An Experimental Study in Cross-Representation Mediation of User Models","authors":"F. Cena, Cristina Gena, Claudia Picardi","doi":"10.1145/2930238.2930263","DOIUrl":"https://doi.org/10.1145/2930238.2930263","url":null,"abstract":"The paper presents the result on cross-representation mediation of user models in the context of movie recommendation. We analyze the possibility of initializing the user models for a content-based recommender starting from movie ratings provided by users in other social applications. We focus in particular on (i) an approach for inferring user model preferences from rating and (ii) the experimentation of several methods to solve the missing value problem exploiting community-based ratings. We tested different variations of the proposed approach exploiting a subset of the MovieLens 10M Dataset, computing rating predictions, and MAE.","PeriodicalId":339100,"journal":{"name":"Proceedings of the 2016 Conference on User Modeling Adaptation and Personalization","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130009918","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
Tell Me What You See, I Will Tell You What You Remember 告诉我你看到了什么，我就告诉你你记得什么
Proceedings of the 2016 Conference on User Modeling Adaptation and Personalization Pub Date : 2016-07-13 DOI: 10.1145/2930238.2930265
F. Marchal, Sylvain Castagnos, A. Boyer
{"title":"Tell Me What You See, I Will Tell You What You Remember","authors":"F. Marchal, Sylvain Castagnos, A. Boyer","doi":"10.1145/2930238.2930265","DOIUrl":"https://doi.org/10.1145/2930238.2930265","url":null,"abstract":"Recommender systems usually rely on users' preferences. Nevertheless, there are many situations (e-learning, e-health) where recommendations should rather be based on their memory. So as to infer in real time and with low involvement what has been memorized by users, we propose in this paper to establish a link between gaze features and visual memory. We designed a user experiment where 24 subjects had to remember 72 images. In the meantime, we collected 18,643 fixation points. Among other metrics, our results show a strong correlation between the relative path angles and the memorized items.","PeriodicalId":339100,"journal":{"name":"Proceedings of the 2016 Conference on User Modeling Adaptation and Personalization","volume":"67 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133672017","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: 5
Lifestyle Recommendations for Hypertension through Rasch-based Feasibility Modeling 基于rasch可行性模型的高血压生活方式建议
Proceedings of the 2016 Conference on User Modeling Adaptation and Personalization Pub Date : 2016-07-13 DOI: 10.1145/2930238.2930251
M. Radha, M. Willemsen, M. Boerhof, W. Ijsselsteijn
{"title":"Lifestyle Recommendations for Hypertension through Rasch-based Feasibility Modeling","authors":"M. Radha, M. Willemsen, M. Boerhof, W. Ijsselsteijn","doi":"10.1145/2930238.2930251","DOIUrl":"https://doi.org/10.1145/2930238.2930251","url":null,"abstract":"In this work we investigate the use of behavior feasibility to adapt and personalize lifestyle-targeting recommender systems for the prevention and treatment of hypertension. Based on survey data (N=300) we model the feasibiliy of 63 behaviors through a Rasch model, describing the engagement in a behavior as a function of the behavior's difficulty and the person's ability. We formulate two feasibility-tailored recommendation strategies that utilize the Rasch model. The engagement maximization strategy aims at maximizing the probability of engagement by proposing very feasible behaviors while the motivation maximization strategy aims to challenge users by matching the difficulty of the advice with the ability of the user, thereby maximizing motivation. In an online study (N=150) we assessed user preference for either strategies (embodied as virtual coaches) in comparison with a random control strategy. Our results show that coaches selecting feasible health advice resonate better with the patient than control. In general patients significantly preferred the engagement maximization strategy over random advice on most factors, while patients with a medium level of ability significantly preferred the motivation maximization strategy on all factors.","PeriodicalId":339100,"journal":{"name":"Proceedings of the 2016 Conference on User Modeling Adaptation and Personalization","volume":"239 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131875273","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: 24
The New Challenges when Modeling Context through Diversity over Time in Recommender Systems 在推荐系统中，通过时间的多样性对上下文建模的新挑战
Proceedings of the 2016 Conference on User Modeling Adaptation and Personalization Pub Date : 2016-07-13 DOI: 10.1145/2930238.2930370
A. L'Huillier, Sylvain Castagnos, A. Boyer
{"title":"The New Challenges when Modeling Context through Diversity over Time in Recommender Systems","authors":"A. L'Huillier, Sylvain Castagnos, A. Boyer","doi":"10.1145/2930238.2930370","DOIUrl":"https://doi.org/10.1145/2930238.2930370","url":null,"abstract":"The main goal of recommender systems is to help users to filter all the information available by suggesting items they may like without they had to find them by themselves. Although the rating prediction is a pretty well controlled topic, being able to make a recommendation at the right moment still remain a challenging task. To this end, most researches try to integrate contextual information (weather, mood, location of users, etc.) in the recommendation process. Even if this process increases users satisfaction, using personal information faces with users' privacy issues. In a different way, our approach is only giving credits to the evolution of diversity within the recent history of consultations, allowing us to automatically detect implicit contexts. In this paper, we will discuss the scientific challenges to be overcome to take maximum advantage of those implicit contexts in the recommendation process.","PeriodicalId":339100,"journal":{"name":"Proceedings of the 2016 Conference on User Modeling Adaptation and Personalization","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124429177","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
Agent-Based Personalisation and User Modeling for Personalised Educational Games 个性化教育游戏的基于代理的个性化和用户建模
Proceedings of the 2016 Conference on User Modeling Adaptation and Personalization Pub Date : 2016-07-13 DOI: 10.1145/2930238.2930273
Marieke M. M. Peeters, K. Bosch, J. Meyer, Mark Antonius Neerincx
{"title":"Agent-Based Personalisation and User Modeling for Personalised Educational Games","authors":"Marieke M. M. Peeters, K. Bosch, J. Meyer, Mark Antonius Neerincx","doi":"10.1145/2930238.2930273","DOIUrl":"https://doi.org/10.1145/2930238.2930273","url":null,"abstract":"Personalisation can increase the learning efficacy of educational games by tailoring their content to the needs of the individual learner. This paper presents the Personalised Educational Game Architecture (PEGA). It uses a multi-agent organisation and an ontology to offer learners personalised training in a game environment. The multi-agent organisation's flexibility enables adaptive automation; the instructor can decide to control only parts of the training, while leaving the rest to the intelligent agents.","PeriodicalId":339100,"journal":{"name":"Proceedings of the 2016 Conference on User Modeling Adaptation and Personalization","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124471799","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
Moodplay: Interactive Mood-based Music Discovery and Recommendation Moodplay:基于情绪的互动式音乐发现和推荐
Proceedings of the 2016 Conference on User Modeling Adaptation and Personalization Pub Date : 2016-07-13 DOI: 10.1145/2930238.2930280
I. Andjelkovic, Denis Parra, J. O'Donovan
{"title":"Moodplay: Interactive Mood-based Music Discovery and Recommendation","authors":"I. Andjelkovic, Denis Parra, J. O'Donovan","doi":"10.1145/2930238.2930280","DOIUrl":"https://doi.org/10.1145/2930238.2930280","url":null,"abstract":"A large body of research in recommender systems focuses on optimizing prediction and ranking. However, recent work has highlighted the importance of other aspects of the recommendations, including transparency, control and user experience in general. Building on these aspects, we introduce MoodPlay, a hybrid recommender system music which integrates content and mood-based filtering in an interactive interface. We show how MoodPlay allows the user to explore a music collection by latent affective dimensions, and we explain how to integrate user input at recommendation time with predictions based on a pre-existing user profile. Results of a user study (N=240) are discussed, with four conditions being evaluated with varying degrees of visualization, interaction and control. Results show that visualization and interaction in a latent space improve acceptance and understanding of both metadata and item recommendations. However, too much of either can result in cognitive overload and a negative impact on user experience.","PeriodicalId":339100,"journal":{"name":"Proceedings of the 2016 Conference on User Modeling Adaptation and Personalization","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124704429","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: 64
On the Impact of Personality in Massive Open Online Learning 论个性对大规模开放式在线学习的影响
Proceedings of the 2016 Conference on User Modeling Adaptation and Personalization Pub Date : 2016-07-13 DOI: 10.1145/2930238.2930240
Guanliang Chen, Dan Davis, C. Hauff, G. Houben
{"title":"On the Impact of Personality in Massive Open Online Learning","authors":"Guanliang Chen, Dan Davis, C. Hauff, G. Houben","doi":"10.1145/2930238.2930240","DOIUrl":"https://doi.org/10.1145/2930238.2930240","url":null,"abstract":"Massive Open Online Courses (MOOCs) have gained considerable momentum since their inception in 2011. They are, however, plagued by two issues that threaten their future: learner engagement and learner retention. MOOCs regularly attract tens of thousands of learners, though only a very small percentage complete them successfully. In the traditional classroom setting, it has been established that personality impacts different aspects of learning. It is an open question to what extent this finding translates to MOOCs: do learners' personalities impact their learning & learning behaviour in the MOOC setting? In this paper, we explore this question and analyse the personality profiles and learning traces of hundreds of learners that have taken a EX101x Data Analysis MOOC on the edX platform. We find learners' personality traits to only weakly correlate with learning as captured through the data traces learners leave on edX.","PeriodicalId":339100,"journal":{"name":"Proceedings of the 2016 Conference on User Modeling Adaptation and Personalization","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130415956","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: 35
On the Value of Reminders within E-Commerce Recommendations 论电子商务推荐中提醒的价值
Proceedings of the 2016 Conference on User Modeling Adaptation and Personalization Pub Date : 2016-07-13 DOI: 10.1145/2930238.2930244
Lukas Lerche, D. Jannach, Malte Ludewig
{"title":"On the Value of Reminders within E-Commerce Recommendations","authors":"Lukas Lerche, D. Jannach, Malte Ludewig","doi":"10.1145/2930238.2930244","DOIUrl":"https://doi.org/10.1145/2930238.2930244","url":null,"abstract":"Most research in recommender systems is focused on the problem of identifying and ranking items that are relevant for the individual users but unknown to them. The potential value of such systems is to help users discover new items, e.g., in e-commerce settings. Many real-world systems however also utilize recommendation lists for a different goal, namely to remind users of items that they have viewed or consumed in the past. In this work, we aim to quantify the value of such reminders in recommendation lists (\"recominders\"), which has to our knowledge not been done in the past. We first report the results of a live experiment in which we applied a naive reminding strategy on an online platform and compare them with results obtained through different offline analyses. We then propose more elaborate reminding techniques, which aim to avoid reminders of too obvious or of already outdated items. Overall, our results show that although reminders do not lead to new item discoveries, they can be valuable both for users and service providers.","PeriodicalId":339100,"journal":{"name":"Proceedings of the 2016 Conference on User Modeling Adaptation and Personalization","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129223433","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: 49
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