Nigel Bosch, Eddie Huang, Lawrence Angrave, M. Perry
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.
{"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}
Cesare Bernardis, Maurizio Ferrari Dacrema, P. Cremonesi
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.
{"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}
Shir Frumerman, Guy Shani, Bracha Shapira, Oren Sar Shalom
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.
{"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}
Matthias Wölbitsch, Simon Walk, M. Goller, D. Helic
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%.
{"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}
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.
{"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}
Since the advent of Web 2.0, Online Social Networks (OSNs) represent a rich opportunity for researchers to collect real user data and to explore OSNs user behaviour. Based on the current challenges and future directions proposed in literature, we aim to investigate how to comprehensively model OSNs user behaviours, by exploiting and combining user data of different nature. We propose to use hypergraphs as a model to easily analyse and combine structural, semantic, and activity-related user information, and to study their evolution over time. This novel user behaviour modelling technique will converge in open, efficient, and scalable libraries, which will be integrated into a modular framework able to handle the data crawling process from several OSNs.
{"title":"Towards an Exhaustive Framework for Online Social Networks User Behaviour Modelling","authors":"Alessia Antelmi","doi":"10.1145/3320435.3323466","DOIUrl":"https://doi.org/10.1145/3320435.3323466","url":null,"abstract":"Since the advent of Web 2.0, Online Social Networks (OSNs) represent a rich opportunity for researchers to collect real user data and to explore OSNs user behaviour. Based on the current challenges and future directions proposed in literature, we aim to investigate how to comprehensively model OSNs user behaviours, by exploiting and combining user data of different nature. We propose to use hypergraphs as a model to easily analyse and combine structural, semantic, and activity-related user information, and to study their evolution over time. This novel user behaviour modelling technique will converge in open, efficient, and scalable libraries, which will be integrated into a modular framework able to handle the data crawling process from several OSNs.","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":"128440354","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}
Tourist groups exploring a city often face the problem of finding a sequence of points of interest that satisfies all group members. In this work, we present three different configurations of a group recommender system that suggests such trips even when tourists are already traveling: connecting multiple smartphones, sharing a public display, and combining both devices in a distributed user interface approach. We conducted a large user study with real groups to evaluate these configurations. Our results show that public displays are attractive for users who prefer an open discussion of their preferences. However, we have empirical evidence that decisions on group preferences often tend to be unfair for some group members, especially when they do not know each other very well. A distributed recommender system aggregating group members' individual preferences fairly with the option to display selected content on a public display was the most appreciated solution for overcoming this problem.
{"title":"A User Study on Groups Interacting with Tourist Trip Recommender Systems in Public Spaces","authors":"Daniel Herzog, W. Wörndl","doi":"10.1145/3320435.3320449","DOIUrl":"https://doi.org/10.1145/3320435.3320449","url":null,"abstract":"Tourist groups exploring a city often face the problem of finding a sequence of points of interest that satisfies all group members. In this work, we present three different configurations of a group recommender system that suggests such trips even when tourists are already traveling: connecting multiple smartphones, sharing a public display, and combining both devices in a distributed user interface approach. We conducted a large user study with real groups to evaluate these configurations. Our results show that public displays are attractive for users who prefer an open discussion of their preferences. However, we have empirical evidence that decisions on group preferences often tend to be unfair for some group members, especially when they do not know each other very well. A distributed recommender system aggregating group members' individual preferences fairly with the option to display selected content on a public display was the most appreciated solution for overcoming this problem.","PeriodicalId":254537,"journal":{"name":"Proceedings of the 27th ACM Conference on User Modeling, Adaptation and Personalization","volume":"11 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":"117324000","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}
M. Lopatka, Victor Ng, B. Miroglio, David Zeber, Alessio Pierluigi Placitelli, L. Thomson
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.
{"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}
{"title":"Session details: Doctoral Consortium","authors":"L. Rook, M. Zanker","doi":"10.1145/3340229","DOIUrl":"https://doi.org/10.1145/3340229","url":null,"abstract":"","PeriodicalId":254537,"journal":{"name":"Proceedings of the 27th ACM Conference on User Modeling, Adaptation and Personalization","volume":"7 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":"123709852","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}
Recommender system helps users to reduce information overload. In recent years, enhancing explainability in recommender systems has drawn more and more attention in the field of Human-Computer Interaction (HCI). However, it is not clear whether a user-preferred explanation interface can maintain the same level of performance while the users are exploring or comparing the recommendations. In this paper, we introduced a participatory process of designing explanation interfaces with multiple explanatory goals for three similarity-based recommendation models. We investigate the relations of user perception and performance with two user studies. In the first study (N=15), we conducted card-sorting and semi-interview to identify the user preferred interfaces. In the second study (N=18), we carry out a performance-focused evaluation of six explanation interfaces. The result suggests that the user-preferred interface may not guarantee the same level of performance.
{"title":"Evaluating Visual Explanations for Similarity-Based Recommendations: User Perception and Performance","authors":"Chun-Hua Tsai, Peter Brusilovsky","doi":"10.1145/3320435.3320465","DOIUrl":"https://doi.org/10.1145/3320435.3320465","url":null,"abstract":"Recommender system helps users to reduce information overload. In recent years, enhancing explainability in recommender systems has drawn more and more attention in the field of Human-Computer Interaction (HCI). However, it is not clear whether a user-preferred explanation interface can maintain the same level of performance while the users are exploring or comparing the recommendations. In this paper, we introduced a participatory process of designing explanation interfaces with multiple explanatory goals for three similarity-based recommendation models. We investigate the relations of user perception and performance with two user studies. In the first study (N=15), we conducted card-sorting and semi-interview to identify the user preferred interfaces. In the second study (N=18), we carry out a performance-focused evaluation of six explanation interfaces. The result suggests that the user-preferred interface may not guarantee the same level of performance.","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":"130669463","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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