Proceedings of the 31st ACM Conference on User Modeling, Adaptation and Personalization最新文献

Temporal-Weighted Bipartite Graph Model for Sparse Expert Recommendation in Community Question Answering 社区问答中稀疏专家推荐的时间加权二部图模型

Proceedings of the 31st ACM Conference on User Modeling, Adaptation and Personalization Pub Date : 2023-06-18 DOI: 10.1145/3565472.3592957

Vaibhav Krishna, Nino Antulov-Fantulin

{"title":"Temporal-Weighted Bipartite Graph Model for Sparse Expert Recommendation in Community Question Answering","authors":"Vaibhav Krishna, Nino Antulov-Fantulin","doi":"10.1145/3565472.3592957","DOIUrl":"https://doi.org/10.1145/3565472.3592957","url":null,"abstract":"Community Question Answering (CQA) websites are valuable knowledge repositories where individuals exchange information by asking and answering questions. With an ever-increasing number of questions and high in-flow and out-flow of users in these communities, a key challenge is to design effective strategies for recommending experts for new questions. This requires robust approaches that facilitate modeling users’ expertise given their changing interests and sparse historical data, at the same time being computationally less expensive for periodic updates. In this paper, we propose a simple graph diffusion-based expert recommendation model for CQA, that can outperform state-of-the-art convolutional neural network and transformers-based deep learning representatives and collaborative models. Our proposed method learns users’ expertise in the context of both semantic and temporal information to capture their changing interests and activity levels with time. Experiments on six real-world datasets from the Stack Exchange network demonstrate that our approach outperforms competitive baseline methods. Further, experiments on cold-start users (users with a limited historical record) show our model achieves an average of 50% performance gain compared to the best baseline method.","PeriodicalId":309249,"journal":{"name":"Proceedings of the 31st ACM Conference on User Modeling, Adaptation and Personalization","volume":"14 2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128669109","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

Human Expectations and Perceptions of Learning in Machine Teaching 机器教学中人类对学习的期望和感知

Proceedings of the 31st ACM Conference on User Modeling, Adaptation and Personalization Pub Date : 2023-06-18 DOI: 10.1145/3565472.3595612

Swati Mishra, Jeffrey M. Rzeszotarski

引用次数: 0

Leveraging Causal Inference to Measure the Impact of a Mental Health App on Users’ Well-being 利用因果推理来衡量心理健康应用程序对用户幸福感的影响

Proceedings of the 31st ACM Conference on User Modeling, Adaptation and Personalization Pub Date : 2023-06-18 DOI: 10.1145/3565472.3592967

Aleix Ruiz de Villa, Gabriele Sottocornola, Ludovik Çoba, Giovanni Maffei, Federico Lucchesi, João Guerreiro, B. Skorulski

{"title":"Leveraging Causal Inference to Measure the Impact of a Mental Health App on Users’ Well-being","authors":"Aleix Ruiz de Villa, Gabriele Sottocornola, Ludovik Çoba, Giovanni Maffei, Federico Lucchesi, João Guerreiro, B. Skorulski","doi":"10.1145/3565472.3592967","DOIUrl":"https://doi.org/10.1145/3565472.3592967","url":null,"abstract":"As stated in the United Nations’ Sustainable Development Goals, poor mental well-being is one of the biggest problems we are facing worldwide. One possible way of addressing it is through interventions delivered via digital devices since they are scalable, ubiquitous and inexpensive. This is also confirmed by the ever-growing plethora of e-health mobile apps being developed. Although these apps rely to some extent on scientific bases, there is still much work to do to understand the effect of specific digital interventions on app users. To shed light on these effects, we ask what types of interventions within the app have the most significant impact on well-being, and to what extent longer engagement leads to improved outcomes. These questions could be answered with dedicated Randomized Controlled Trials (RCTs), which are generally expensive, time-consuming, and single-purposed. To overcome these difficulties, we adopt instrumental variables on a combination of data collected in an RCT, behavioural data from the app, and a randomized recommender system, to evaluate intervention and app dose-response effects on users’ self-reported well-being. Thus, we present a general causal inference approach for extending results from collected data in RCTs applied in the context of digital health intervention. Following this approach, we show how to measure the impact of different types of activities on the users’ well-being. This allows us to identify the most impactful activities in the app (namely, sleep and relaxation activities), which have direct implications for the app design. On the other hand, we prove the positive effect of longer app usage.","PeriodicalId":309249,"journal":{"name":"Proceedings of the 31st ACM Conference on User Modeling, Adaptation and Personalization","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131820127","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

How To Model Users Through Their Abilities: A Methodological Perspective On Ability-Based Design 如何通过用户的能力建模:基于能力设计的方法论视角

Proceedings of the 31st ACM Conference on User Modeling, Adaptation and Personalization Pub Date : 2023-06-18 DOI: 10.1145/3565472.3595608

Amelie Nolte

{"title":"How To Model Users Through Their Abilities: A Methodological Perspective On Ability-Based Design","authors":"Amelie Nolte","doi":"10.1145/3565472.3595608","DOIUrl":"https://doi.org/10.1145/3565472.3595608","url":null,"abstract":"Ability-Based Design offers a design approach to support practitioners in creating accessible and indivually optimized systems through a shift in focus on abilities instead of disabilities. As such, it could help to minimize moments of exclusion through technology and offer support for people, especially during times of crisis or social distancing as caused by the COVID-19 pandemic. However, in its current theoretically coined state, the approach includes no instruction on how to achieve this shift in mindset and how to apply a focus on abilities when designing systems. Within my thesis, I address this gap in research by analyzing ways for modeling users based on their abilities. For this, I propose ability models as user representation, identify the challenges as well as potentials of such ability models, and examine how this approach for the personalization of systems can help and support user populations. The overall aim and expected benefit of this research is to provide adequate techniques on how to include abilities into design considerations as well as an understanding of what accommodating for abilities might change, achieve or effect in terms of HCI research and design.","PeriodicalId":309249,"journal":{"name":"Proceedings of the 31st ACM Conference on User Modeling, Adaptation and Personalization","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130817271","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

Scalable and Explainable Linear Shallow Autoencoders for Collaborative Filtering from Industrial Perspective 从工业角度看协同滤波的可扩展和可解释的线性浅自编码器

Proceedings of the 31st ACM Conference on User Modeling, Adaptation and Personalization Pub Date : 2023-06-18 DOI: 10.1145/3565472.3595630

Vojtěch Vančura

引用次数: 0

Composing Groups in Collaborative Learning by Pair Personality Differences 基于个性差异的合作学习小组组成

Proceedings of the 31st ACM Conference on User Modeling, Adaptation and Personalization Pub Date : 2023-06-18 DOI: 10.1145/3565472.3592954

Akihiro Kobayashi, Yuichi Ishikawa, K. Ikeda, D. Kamisaka, R. Legaspi

{"title":"Composing Groups in Collaborative Learning by Pair Personality Differences","authors":"Akihiro Kobayashi, Yuichi Ishikawa, K. Ikeda, D. Kamisaka, R. Legaspi","doi":"10.1145/3565472.3592954","DOIUrl":"https://doi.org/10.1145/3565472.3592954","url":null,"abstract":"Previous studies have shown that the personality composition of a group significantly affects learners’ satisfaction during collaborative learning. However, while these studies investigated a group as a whole by focusing on group statistics, such as the mean and standard deviation of the members’ personalities, they paid little attention to the personality differences of individual pairs within the group, albeit the group contains many pairwise interactions. In this paper, we studied whether and how pairwise personality differences between a learner and groupmates affect the learner’s satisfaction. Examining data collected from an employee training program during which learners had reflective group discussions, we confirmed that pairwise personality differences significantly affect a learner’s level of satisfaction in the program. Specifically, satisfaction is affected by (1) the average of the personality differences between the learner and each individual groupmate, which reflects the degree to which the learner is different from the groupmates on average, and (2) the personality difference from the groupmate who has the most different/similar personality from/to the learner.","PeriodicalId":309249,"journal":{"name":"Proceedings of the 31st ACM Conference on User Modeling, Adaptation and Personalization","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132396418","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

Amplifying Artists’ Voices: Item Provider Perspectives on Influence and Fairness of Music Streaming Platforms 放大艺术家的声音:项目提供商对音乐流媒体平台的影响力和公平性的看法

Proceedings of the 31st ACM Conference on User Modeling, Adaptation and Personalization Pub Date : 2023-06-18 DOI: 10.1145/3565472.3592960

Karlijn Dinnissen, Christine Bauer

{"title":"Amplifying Artists’ Voices: Item Provider Perspectives on Influence and Fairness of Music Streaming Platforms","authors":"Karlijn Dinnissen, Christine Bauer","doi":"10.1145/3565472.3592960","DOIUrl":"https://doi.org/10.1145/3565472.3592960","url":null,"abstract":"The majority of music consumption nowadays takes place on music streaming platforms. Whichever artists, albums, or songs are exposed to consumers on these platforms therefore greatly influences what music is ultimately consumed. As a result, the impact of these platforms on artists—their main item providers—is considerable. The recommender systems at the core of streaming platforms, though, have traditionally been developed focusing on end consumer objectives. Only recently, researchers have started to include item provider objectives, though rarely through reaching out to item providers directly. By omitting this important stakeholder’s point of view, we risk not understanding what artists value most, and might miss first-hand ideas on how to improve streaming platforms and recommender systems. Therefore, we conducted semi-structured interviews to capture the artists’ view. Specifically, we explore artists’ considerations regarding fairness, transparency, and diversity in music recommender systems, and the role artists envision for streaming platforms regarding those topics. We identify some topics with a clear consensus among artists, such as desiring more control over which music is recommended to whom, and expecting streaming platforms to actively increase music diversity in recommendations. In contrast, artists’ opinions differ on whether platforms should actively intervene in recommender systems to, e.g., increase localization or gender balance. Further, we observe that artists often take user preferences into account and even suggest new platform functionality to benefit both users and item providers. We encourage utilizing these insights when designing and evaluating music streaming platforms and recommender systems.","PeriodicalId":309249,"journal":{"name":"Proceedings of the 31st ACM Conference on User Modeling, Adaptation and Personalization","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115708829","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

Promoting Tail Item Recommendations in E-Commerce 在电子商务中推广尾项推荐

Proceedings of the 31st ACM Conference on User Modeling, Adaptation and Personalization Pub Date : 2023-06-18 DOI: 10.1145/3565472.3592968

Tamar Didi, Ido Guy, Amit Livne, Arnon Dagan, L. Rokach, Bracha Shapira

{"title":"Promoting Tail Item Recommendations in E-Commerce","authors":"Tamar Didi, Ido Guy, Amit Livne, Arnon Dagan, L. Rokach, Bracha Shapira","doi":"10.1145/3565472.3592968","DOIUrl":"https://doi.org/10.1145/3565472.3592968","url":null,"abstract":"The research area of recommender systems (RS) in e-commerce has become extremely popular in recent years. However, traditional RSs tend to recommend popular items, while niche (long-tail) items are often neglected, which is known as the long-tail problem. However, recent studies found that tail items are one of the key success factors in the e-commerce world. The availability of such items encompasses relatively high marginal profits and boosts the sales of popular short-head items. We suggest promoting long-tail items by leveraging the short-head items’ advantages and exposing the user to a tail item that may have not been considered otherwise. We use a classification model and statistical tools to generate personalized recommendations of a long-tail item considering a short-head item that has already been clicked. The uniqueness of our method lies in the combination of tail and head items to uplift the exposure of the latter and in using an applicable solution to deal with the extreme volume of tail items. We demonstrate the effectiveness of our method on real-world data from eBay and provide an analysis of the long-tail phenomenon and consumption behavior.","PeriodicalId":309249,"journal":{"name":"Proceedings of the 31st ACM Conference on User Modeling, Adaptation and Personalization","volume":"73 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125834874","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

Service-based Presentation of Multimodal Information for the Justification of Recommender Systems Results 基于服务的多模态信息表示，用于推荐系统结果的证明

Proceedings of the 31st ACM Conference on User Modeling, Adaptation and Personalization Pub Date : 2023-06-18 DOI: 10.1145/3565472.3592962

Zhongli Filippo Hu, Noemi Mauro, G. Petrone, L. Ardissono

引用次数: 0

Investigating the effectiveness of persuasive justification messages in fair music recommender systems for users with different personality traits 针对不同人格特征的用户，调查公平音乐推荐系统中说服性辩护信息的有效性

Proceedings of the 31st ACM Conference on User Modeling, Adaptation and Personalization Pub Date : 2023-06-18 DOI: 10.1145/3565472.3592958

Somayeh Fatahi, Mina Mousavifar, Julita Vassileva

{"title":"Investigating the effectiveness of persuasive justification messages in fair music recommender systems for users with different personality traits","authors":"Somayeh Fatahi, Mina Mousavifar, Julita Vassileva","doi":"10.1145/3565472.3592958","DOIUrl":"https://doi.org/10.1145/3565472.3592958","url":null,"abstract":"In recent decades, music recommender systems have become increasingly popular and have attracted a lot of research attention. While there has been significant progress in algorithm design to improve the quality of recommendations for listeners, there are new research challenges arising in large scale systems which have to consider the interests of both listeners and artists. To ensure a sustainable community of artists and a diversity of genres, artists, and songs, the recommender needs to ensure that new artists have a chance to be heard and rated. So, in addition to the objective of optimizing the recommendation to the preferences and enjoyment of the listener, a large scale MRS has a “fairness” objective to provide new artists (the protected group) with an opportunity to be heard. Previous research shows that using persuasive explanations can increase user acceptance of the recommended items. We propose to use persuasive justification messages for songs of new artists to influence user acceptance and satisfaction with these recommendations. The messages are designed to implement the six popular Cialdini persuasive strategies. We explore the effects of different persuasive messages on users with different Big-5 (OCEAN) personality types in an online study (n=205). The findings show that users with different personality traits are receptive to different persuasive messages and suggest how to personalize the persuasive justifications to amplify their effect for users with different personalities. These results can guide the development of personalized/ adaptive persuasive recommendation justifications for fair music recommender systems leading to a better user satisfaction and mitigating the “rich get richer” effect in large-scale music recommender systems, ensuring diversity of content and sustainability of the community.","PeriodicalId":309249,"journal":{"name":"Proceedings of the 31st ACM Conference on User Modeling, Adaptation and Personalization","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129376101","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