Proceedings of the 27th ACM Conference on User Modeling, Adaptation and Personalization最新文献_第2页

Modeling Improvement for Underrepresented Minorities in Online STEM Education 在线STEM教育中未被充分代表的少数民族的建模改进

Proceedings of the 27th ACM Conference on User Modeling, Adaptation and Personalization Pub Date : 2019-06-07 DOI: 10.1145/3320435.3320463

Nigel Bosch, Eddie Huang, Lawrence Angrave, M. Perry

{"title":"Modeling Improvement for Underrepresented Minorities in Online STEM Education","authors":"Nigel Bosch, Eddie Huang, Lawrence Angrave, M. Perry","doi":"10.1145/3320435.3320463","DOIUrl":"https://doi.org/10.1145/3320435.3320463","url":null,"abstract":"Previous research has shown that students from underrepresented minority groups tend to receive lower grades in online classes than their peers, especially in science-focused courses. We propose that there may also be benefits to online courses for these students (e.g., opportunities for peer discussions where minority status is less salient), though little is currently known about these potential benefits. We present a new perspective on learning outcomes by measuring improvement, rather than grades alone. In learning management system data from seven semesters of an online introductory science course, we found that students from underrepresented minority racial groups were indeed less likely to receive high grades, and scored lower on exams; however, their exam scores improved throughout the semester a similar amount compared to their peers. We also compared improvement to students' behaviors, including exam submission times and forum usage, finding that these behaviors were related to improvement. Finally, we also briefly discuss implications of these findings for reducing inequalities in education, and the possibilities for underrepresented minority students in online STEM education in particular.","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":"115827596","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

Estimating Confidence of Individual User Predictions in Item-based Recommender Systems 基于物品的推荐系统中个人用户预测的置信度估计

Proceedings of the 27th ACM Conference on User Modeling, Adaptation and Personalization Pub Date : 2019-06-07 DOI: 10.1145/3320435.3320453

Cesare Bernardis, Maurizio Ferrari Dacrema, P. Cremonesi

{"title":"Estimating Confidence of Individual User Predictions in Item-based Recommender Systems","authors":"Cesare Bernardis, Maurizio Ferrari Dacrema, P. Cremonesi","doi":"10.1145/3320435.3320453","DOIUrl":"https://doi.org/10.1145/3320435.3320453","url":null,"abstract":"This paper focuses on recommender systems based on item-item collaborative filtering (CF). Although research on item-based methods is not new, current literature does not provide any reliable insight on how to estimate confidence of recommendations. The goal of this paper is to fill this gap, by investigating the conditions under which item-based recommendations will succeed or fail for a specific user. We formalize the item-based CF problem as an eigenvalue problem, where estimated ratings are equivalent to the true (unknown) ratings multiplied by a user-specific eigenvalue of the similarity matrix. We show that the magnitude of the eigenvalue related to a user is proportional to the accuracy of recommendations for that user. We define a confidence parameter called the eigenvalue confidence index, analogous to the eigenvalue of the similarity matrix, but simpler to be computed. We also show how to extend the eigenvalue confidence index to matrix-factorization algorithms. A comprehensive set of experiments on five datasets show that the eigenvalue confidence index is effective in predicting, for each user, the quality of recommendations. On average, our confidence index is 3 times more correlated with MAP with respect to previous confidence estimates.","PeriodicalId":254537,"journal":{"name":"Proceedings of the 27th ACM Conference on User Modeling, Adaptation and Personalization","volume":"56 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":"123383104","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: 7

Are All Rejected Recommendations Equally Bad?: Towards Analysing Rejected Recommendations 所有被拒绝的推荐都一样糟糕吗?:分析被拒绝的建议

Proceedings of the 27th ACM Conference on User Modeling, Adaptation and Personalization Pub Date : 2019-06-07 DOI: 10.1145/3320435.3320448

Shir Frumerman, Guy Shani, Bracha Shapira, Oren Sar Shalom

{"title":"Are All Rejected Recommendations Equally Bad?: Towards Analysing Rejected Recommendations","authors":"Shir Frumerman, Guy Shani, Bracha Shapira, Oren Sar Shalom","doi":"10.1145/3320435.3320448","DOIUrl":"https://doi.org/10.1145/3320435.3320448","url":null,"abstract":"When evaluating algorithms that recommend a list of relevant items to a user, it is common to use metrics such as precision to measure the system accuracy. When computing precision, one computes the number of items that were selected by the user among the recommended items. As such, recommended items that were not selected by the user, which we call em rejected recommendations, are all considered to be bad recommendations, resulting in no increase to the system accuracy metric. Our ultimate goal is to develop a new recommendation accuracy evaluation metric, which may assign some value to the rejected recommendations. In this paper, as a first step, we claim that some rejected recommendations are better than others. Specifically, we consider items that are similar to the item that was finally selected, as better recommendations than items that bear little similarity. We conduct a user study, showing that rejected recommendations that have high content or collaborative similarity to the selected item are perceived by users as better recommendations than items with low similarity. In addition, we study the correlations between the recommended items shown to a user and the un-recommended items that the user has selected in a real-life job posting dataset. We show that when considering item similarity rather than simple precision, the correlations are much higher. This may be attributed to the influence of the recommended items on the decisions of the user.","PeriodicalId":254537,"journal":{"name":"Proceedings of the 27th ACM Conference on User Modeling, Adaptation and Personalization","volume":"22 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":"130059605","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

Beggars Can't Be Choosers: Augmenting Sparse Data for Embedding-Based Product Recommendations in Retail Stores 乞丐不能挑挑拣拣:基于嵌入的零售商店产品推荐的增强稀疏数据

Proceedings of the 27th ACM Conference on User Modeling, Adaptation and Personalization Pub Date : 2019-06-07 DOI: 10.1145/3320435.3320454

Matthias Wölbitsch, Simon Walk, M. Goller, D. Helic

{"title":"Beggars Can't Be Choosers: Augmenting Sparse Data for Embedding-Based Product Recommendations in Retail Stores","authors":"Matthias Wölbitsch, Simon Walk, M. Goller, D. Helic","doi":"10.1145/3320435.3320454","DOIUrl":"https://doi.org/10.1145/3320435.3320454","url":null,"abstract":"Recommender systems are an essential component in many e-commerce platforms to drive sales and guide customers when exploring new products. With the increasing adoption of RFID technology in traditional brick-and-mortar stores, for example, in the form of smart fitting rooms that allow to display recommendations in the integrated mirror, retailers have only recently started to tap into existing product recommendation algorithms. However, due to limited data availability as well as sparsity, for example due to assortments adapted for different demographics, traditional retailers largely struggle to leverage this technology. In this paper we extend the state-of-the-art embedding-based recommender approach prod2vec by processing information about co-purchased products (i.e., shopping baskets) in retail stores. By adding point-of-sale information to shopping baskets we are able to provide recommendations aimed at individual stores, without having to maintain separate models for each location. Furthermore, we experiment with data augmentation methods to overcome the imposed limitations of the available data, and are able to increase the quality of the computed recommendations by more than 6.9%.","PeriodicalId":254537,"journal":{"name":"Proceedings of the 27th ACM Conference on User Modeling, Adaptation and Personalization","volume":"1 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":"130374397","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: 10

Modeling Behavior and Designing Evidence-Based Technologies: What We Can Learn for Empirical Data 行为建模和基于证据的技术设计:我们可以从经验数据中学到什么

Proceedings of the 27th ACM Conference on User Modeling, Adaptation and Personalization Pub Date : 2019-06-07 DOI: 10.1145/3320435.3323708

M. Avraamides

{"title":"Modeling Behavior and Designing Evidence-Based Technologies: What We Can Learn for Empirical Data","authors":"M. Avraamides","doi":"10.1145/3320435.3323708","DOIUrl":"https://doi.org/10.1145/3320435.3323708","url":null,"abstract":"To be effective, modern technological applications should take into account the needs, preferences, capabilities, and limitations of human users. In recent years, this requirement has made more imperative the need to understand in more detail the human cognition and its constraints. Cognitive processes and their underlying neural substrates are traditionally investigated with laboratory studies that yield data of different forms, ranging from accuracy and reaction time data in behavioural experiments to electrophysiological responses and neuro-imaging data in neuroscience studies. But how do such data enable psychologists and other scientists to draw conclusions about cognition? Also, how can the extracted knowledge be exploited for the design of evidence-based smart systems and innovative technologies? In this talk, I will address these questions by drawing examples from my research that employs various methods and techniques, including behavioural experiments in Virtual Reality, eye-tracking, and physiological recordings. Although most of this research focuses on how people attend, perceive, and memorize spatial information, studies investigating more general cognitive mechanisms (e.g., selective attention and executive functions) will be also presented.","PeriodicalId":254537,"journal":{"name":"Proceedings of the 27th ACM Conference on User Modeling, Adaptation and Personalization","volume":"1 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":"130762243","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

Towards an Exhaustive Framework for Online Social Networks User Behaviour Modelling 面向在线社交网络用户行为建模的详尽框架

Proceedings of the 27th ACM Conference on User Modeling, Adaptation and Personalization Pub Date : 2019-06-07 DOI: 10.1145/3320435.3323466

Alessia Antelmi

引用次数: 4

A User Study on Groups Interacting with Tourist Trip Recommender Systems in Public Spaces 公共空间旅游团与旅游推荐系统互动的用户研究

Proceedings of the 27th ACM Conference on User Modeling, Adaptation and Personalization Pub Date : 2019-06-07 DOI: 10.1145/3320435.3320449

Daniel Herzog, W. Wörndl

引用次数: 19

Telemetry-Aware Add-on Recommendation for Web Browser Customization 遥测感知附加组件推荐的Web浏览器定制

Proceedings of the 27th ACM Conference on User Modeling, Adaptation and Personalization Pub Date : 2019-06-07 DOI: 10.1145/3320435.3320450

M. Lopatka, Victor Ng, B. Miroglio, David Zeber, Alessio Pierluigi Placitelli, L. Thomson

{"title":"Telemetry-Aware Add-on Recommendation for Web Browser Customization","authors":"M. Lopatka, Victor Ng, B. Miroglio, David Zeber, Alessio Pierluigi Placitelli, L. Thomson","doi":"10.1145/3320435.3320450","DOIUrl":"https://doi.org/10.1145/3320435.3320450","url":null,"abstract":"Web Extensions (add-ons) allow clients to customize their Web browsing experience through the addition of auxiliary features to their browsers. The add-on ecosystem is a market differentiator for the Firefox browser, offering contributions from both commercial entities and community developers. In this paper, we present the Telemetry-Aware Add-on Recommender (TAAR), a system for recommending add-ons to Firefox users by leveraging separate models trained to three main sources of user data: the set of add-ons a user already has installed; usage and interaction data (browser Telemetry); and the language setting of the user's browser (locale). We build individual recommendation models for each of these data sources, and combine the recommendations they generate using a linear stacking ensemble method. Our method employs a novel penalty function for tuning weight parameters, which is adapted from the log likelihood ratio cost function, allowing us to scale the penalty of both correct and incorrect recommendations using the confidence weights associated with the individual component model recommendations. This modular approach provides a way to offer relevant personalized recommendations while respecting Firefox's granular privacy preferences and adhering to Mozilla's lean data collection policy. To evaluate our recommender system, we ran a large-scale randomized experiment that was deployed to 350,000 Firefox users and localized to 11 languages. We found that, overall, users were 4.4% more likely to install add-ons recommended by our ensemble method compared to a curated list. Furthermore, the magnitude of the increase varies significantly across locales, achieving over 8% improvement among German-language users.","PeriodicalId":254537,"journal":{"name":"Proceedings of the 27th ACM Conference on User Modeling, Adaptation and Personalization","volume":"54 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":"115383799","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

Session details: Doctoral Consortium 会议详情:博士联盟

Proceedings of the 27th ACM Conference on User Modeling, Adaptation and Personalization Pub Date : 2019-06-07 DOI: 10.1145/3340229

L. Rook, M. Zanker

引用次数: 0

Evaluating Visual Explanations for Similarity-Based Recommendations: User Perception and Performance 评估基于相似性的推荐的视觉解释:用户感知和性能

Proceedings of the 27th ACM Conference on User Modeling, Adaptation and Personalization Pub Date : 2019-06-07 DOI: 10.1145/3320435.3320465

Chun-Hua Tsai, Peter Brusilovsky

引用次数: 24