Proceedings of the 16th ACM Conference on Recommender Systems最新文献

Zillow: Volume Governing for Email and Push Messages Zillow:电子邮件和推送消息的音量控制

Proceedings of the 16th ACM Conference on Recommender Systems Pub Date : 2022-09-18 DOI: 10.1145/3523227.3547399

E. P. Nichols, Ruomeng Xu, Balasubramanian Thiagarajan, Shruti Kamath

引用次数: 0

Don’t recommend the obvious: estimate probability ratios 不要推荐那些显而易见的方法:估计概率比率

Proceedings of the 16th ACM Conference on Recommender Systems Pub Date : 2022-09-18 DOI: 10.1145/3523227.3546753

Roberto Pellegrini, Wenjie Zhao, Iain Murray

{"title":"Don’t recommend the obvious: estimate probability ratios","authors":"Roberto Pellegrini, Wenjie Zhao, Iain Murray","doi":"10.1145/3523227.3546753","DOIUrl":"https://doi.org/10.1145/3523227.3546753","url":null,"abstract":"Sequential recommender systems are becoming widespread in the online retail and streaming industry. These systems are often trained to predict the next item given a sequence of a user’s recent actions, and standard evaluation metrics reward systems that can identify the most probable items that might appear next. However, some recent papers instead evaluate recommendation systems with popularity-sampled metrics, which measure how well the model can find a user’s next item when hidden amongst generally-popular items. We argue that these popularity-sampled metrics are more appropriate for recommender systems, because the most probable items for a user often include generally-popular items. If the probability that a customer will watch Toy Story is not much more probable than for the average customer, then the movie isn’t especially relevant for them and we should not recommend it. This paper shows that optimizing popularity-sampled metrics is closely related to estimating point-wise mutual information (PMI). We propose and compare two techniques to fit PMI directly, which both improve popularity-sampled metrics for state-of-the-art recommender systems. The improvements are large compared to differences between recently-proposed model architectures.","PeriodicalId":443279,"journal":{"name":"Proceedings of the 16th ACM Conference on Recommender Systems","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125493355","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

Exploring the longitudinal effects of nudging on users’ music genre exploration behavior and listening preferences 探索轻推对用户音乐类型探索行为和聆听偏好的纵向影响

Proceedings of the 16th ACM Conference on Recommender Systems Pub Date : 2022-09-18 DOI: 10.1145/3523227.3546772

Yu Liang, M. Willemsen

{"title":"Exploring the longitudinal effects of nudging on users’ music genre exploration behavior and listening preferences","authors":"Yu Liang, M. Willemsen","doi":"10.1145/3523227.3546772","DOIUrl":"https://doi.org/10.1145/3523227.3546772","url":null,"abstract":"Previous studies on exploration have shown that users can be nudged to explore further away from their current preferences. However, these effects were shown in a single session study, while it often takes time to explore new tastes and develop new preferences. In this work, we present a longitudinal study on users’ exploration behavior and behavior change over time after they have used a music genre exploration tool for four sessions in six weeks. We test two relevant nudges to help them explore more: the starting point (the personalization of the default initial playlist) and the visualization of users’ previous position(s). Our results show that the personalization level of the default initial playlist in the first session influences the preferred personalization level users set in the second session but fades away in later sessions as users start exploring in different directions. Visualization of users’ previous positions did not anchor users to stay closer to the initial defaults. Over time, users perceived the playlist to be more personalized to their tastes and helpful to explore the genre. Perceived helpfulness increased more when users explored further away from their current preferences. Apart from differences in self-reported measures, we also find some objective evidence for preference change in users’ top tracks from their Spotify profile, that over the period of 6 weeks moved somewhat closer to the genre that users selected to explore with the tool.","PeriodicalId":443279,"journal":{"name":"Proceedings of the 16th ACM Conference on Recommender Systems","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116082599","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

Psychology-informed Recommender Systems Tutorial 心理学推荐系统教程

Proceedings of the 16th ACM Conference on Recommender Systems Pub Date : 2022-09-18 DOI: 10.1145/3523227.3547375

E. Lex, M. Schedl

{"title":"Psychology-informed Recommender Systems Tutorial","authors":"E. Lex, M. Schedl","doi":"10.1145/3523227.3547375","DOIUrl":"https://doi.org/10.1145/3523227.3547375","url":null,"abstract":"Recommender systems are essential tools to support human decision-making in online information spaces. Many state-of-the-art recommender systems adopt advanced machine learning techniques to model and predict user preferences from behavioral data. While such systems can provide useful and effective recommendations, their algorithmic design commonly neglects underlying psychological mechanisms that shape user preferences and behavior. In this tutorial, we offer a comprehensive review of the state of the art and progress in psychology-informed recommender systems, i.e., recommender systems that incorporate human cognitive processes, personality, and affective cues into recommendation models, along with definitions, strengths and weaknesses. We show how such systems can improve the recommendation process in a user-centric fashion. With this tutorial, we aim to stimulate more ideas and discussion with the audience on core issues of this topic such as the identification of suitable psychological models, availability of datasets, or the suitability of existing performance metrics to evaluate the efficacy of psychology-informed recommender systems. Besides, we present takeaways to recommender systems practitioners how to build psychology-informed recommender systems. Previous versions of this tutorial were presented, among others, at The ACM Web Conference 2022 and the ACM SIGIR Conference on Human Information Interaction and Retrieval (CHIIR) 2022.","PeriodicalId":443279,"journal":{"name":"Proceedings of the 16th ACM Conference on Recommender Systems","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116488280","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

Improving Recommender Systems with Human-in-the-Loop 利用人在循环改进推荐系统

Proceedings of the 16th ACM Conference on Recommender Systems Pub Date : 2022-09-18 DOI: 10.1145/3523227.3547373

Dmitry Ustalov, N. Fedorova, Nikita Pavlichenko

引用次数: 0

Automate Page Layout Optimization: An Offline Deep Q-Learning Approach 自动页面布局优化:离线深度q -学习方法

Proceedings of the 16th ACM Conference on Recommender Systems Pub Date : 2022-09-18 DOI: 10.1145/3523227.3547400

Zhou Qin, Wenyang Liu

引用次数: 0

Learning Users’ Preferred Visual Styles in an Image Marketplace 在图像市场中学习用户的首选视觉风格

Proceedings of the 16th ACM Conference on Recommender Systems Pub Date : 2022-09-18 DOI: 10.1145/3523227.3547382

Raul Gomez Bruballa, Lauren Burnham-King, Alessandra Sala

引用次数: 1

Personalizing Benefits Allocation Without Spending Money: Utilizing Uplift Modeling in a Budget Constrained Setup 不花钱的个性化利益分配:在预算受限的设置中利用提升模型

Proceedings of the 16th ACM Conference on Recommender Systems Pub Date : 2022-09-18 DOI: 10.1145/3523227.3547381

Dmitri Goldenberg, Javier Albert

引用次数: 1

KA-Recsys: Knowledge Appropriate Patient Focused Recommendation Technologies KA-Recsys:以患者为中心的知识恰当推荐技术

Proceedings of the 16th ACM Conference on Recommender Systems Pub Date : 2022-09-18 DOI: 10.1145/3523227.3547422

Khushboo Thaker

{"title":"KA-Recsys: Knowledge Appropriate Patient Focused Recommendation Technologies","authors":"Khushboo Thaker","doi":"10.1145/3523227.3547422","DOIUrl":"https://doi.org/10.1145/3523227.3547422","url":null,"abstract":"1 MOTIVATION AND GOAL Diseases such as diabetes, cancer and heart disease demand that patients take an active role in disease management and seek health information for decision-making and self-management [1]. The most common methods of providing reliable information to patients include health literacy workshops and patient education materials [12, 15]. However, these materials are prepared for the general patient population and not always tailored to each patient’s specific needs [2, 12]. In addition, patients seek for their information needs through search engines. Previous studies have reported that search engines don’t always support information needs of patients [11]. Consequently, people seek information on disease specific online health communities (OHCs) [9]. But the risk of propagating misinformation still exists because existing OHCs do not provide an infrastructure to help patients find relevant and trustworthy information [8, 19].Existing patient focused health search engines and health recommender systems can accommodate better support of patients’ information needs by providing them trustworthy resources. However, current PHRS are personalized to patients’ interests and so information provided by these layperson-oriented systems is rather general [5, 6]. In fact, previous study has shown that a patient’s personal knowledge about their disease becomes more sophisticated over the course of disease [6, 7]. Therefore, our principal motivation is to fill this gap in current PHRS, by investigating ways to suggest individualized health information that not only adapts to patients’ current information needs but also patients’ knowledge-level across the disease trajectory. The health materials recommended at the level of patients’ knowledge will not only help them engage with materials but also help in informed decision making and self management [16].","PeriodicalId":443279,"journal":{"name":"Proceedings of the 16th ACM Conference on Recommender Systems","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130547914","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

Translating the Public Service Media Remit into Metrics and Algorithms 将公共服务媒体职权转化为度量和算法

Proceedings of the 16th ACM Conference on Recommender Systems Pub Date : 2022-09-18 DOI: 10.1145/3523227.3547380

Andreas Grün, Xenija Neufeld

{"title":"Translating the Public Service Media Remit into Metrics and Algorithms","authors":"Andreas Grün, Xenija Neufeld","doi":"10.1145/3523227.3547380","DOIUrl":"https://doi.org/10.1145/3523227.3547380","url":null,"abstract":"After multiple years of providing automated video recommendations in the ZDFmediathek, ZDF has established a solid ground for the usage of recommender systems. Being a Public Service Media (PSM) provider, our most important driver on this journey is our Public Service Media Remit (PSMR). We are committed to cultivate PSM values such as diversity, fairness, and transparency while providing fresh and relevant content. Therefore, it is important for us to not only measure the success of our recommender systems in terms of basic business Key Performance Indicators (KPIs) such as clicks and viewing minutes but also to ensure and to measure the achievement of PSM values. While speaking about PSM values, however, it is important to keep in mind that there is no easy way to directly measure values as such. In order to be able to measure their extent in a recommender system, we need to translate these values into public value metrics. However, not only the final results are essential for the PSMR. Additionally, it is highly important to establish transparency while working towards these results, that is, while defining the data, the algorithms, and the pipelines used in recommender systems. In our talk we will provide a deeper insight into how we approach this task with Model Cards and give an overview of some models, their Model Cards, and metrics that we are currently using for ZDFmediathek.","PeriodicalId":443279,"journal":{"name":"Proceedings of the 16th ACM Conference on Recommender Systems","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133077728","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