Frequent updates and model retraining are important in various application areas of recommender systems, e.g., news recommendation. Moreover, in such domains, we may not only face the problem of dealing with a constant stream of new data, but also with anonymous users, leading to the problem of streaming session-based recommendation (SSR). Such problem settings have attracted increased interest in recent years, and different deep learning architectures were proposed that support fast updates of the underlying prediction models when new data arrive. In a recent paper, a method based on Graph Neural Networks (GNN) was proposed as being superior than previous methods for the SSR problem. The baselines in the reported experiments included different machine learning models. However, several earlier studies have shown that often conceptually simpler methods, e.g., based on nearest neighbors, can be highly effective for session-based recommendation problems. In this work, we report a similar phenomenon for the streaming configuration. We first reproduce the results of the mentioned GNN method and then show that simpler methods are able to outperform this complex state-of-the-art neural method on two datasets. Overall, our work points to continued methodological issues in the academic community, e.g., in terms of the choice of baselines and reproducibility.1
频繁的更新和模型再训练在推荐系统的各个应用领域都很重要，例如新闻推荐。此外，在这些领域中，我们可能不仅面临处理持续不断的新数据流的问题，而且还面临匿名用户的问题，从而导致基于流会话的推荐(streaming session based recommendation, SSR)问题。近年来，这样的问题设置引起了越来越多的兴趣，并且提出了不同的深度学习架构，以支持在新数据到达时快速更新底层预测模型。在最近的一篇论文中，提出了一种基于图神经网络(GNN)的方法来解决SSR问题。报告实验中的基线包括不同的机器学习模型。然而，一些早期的研究表明，通常概念上更简单的方法，例如，基于最近邻的方法，可以非常有效地解决基于会话的推荐问题。在这项工作中，我们报告了流配置的类似现象。我们首先重现了上述GNN方法的结果，然后表明更简单的方法能够在两个数据集上优于这种复杂的最先进的神经方法。总的来说，我们的工作指出了学术界持续存在的方法问题，例如，在基线的选择和可重复性方面
{"title":"Streaming Session-Based Recommendation: When Graph Neural Networks meet the Neighborhood","authors":"Sara Latifi, D. Jannach","doi":"10.1145/3523227.3548485","DOIUrl":"https://doi.org/10.1145/3523227.3548485","url":null,"abstract":"Frequent updates and model retraining are important in various application areas of recommender systems, e.g., news recommendation. Moreover, in such domains, we may not only face the problem of dealing with a constant stream of new data, but also with anonymous users, leading to the problem of streaming session-based recommendation (SSR). Such problem settings have attracted increased interest in recent years, and different deep learning architectures were proposed that support fast updates of the underlying prediction models when new data arrive. In a recent paper, a method based on Graph Neural Networks (GNN) was proposed as being superior than previous methods for the SSR problem. The baselines in the reported experiments included different machine learning models. However, several earlier studies have shown that often conceptually simpler methods, e.g., based on nearest neighbors, can be highly effective for session-based recommendation problems. In this work, we report a similar phenomenon for the streaming configuration. We first reproduce the results of the mentioned GNN method and then show that simpler methods are able to outperform this complex state-of-the-art neural method on two datasets. Overall, our work points to continued methodological issues in the academic community, e.g., in terms of the choice of baselines and reproducibility.1","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":"122845736","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}
Behnam Rahdari, Peter Brusilovsky, Daqing He, Khushboo Thaker, Zhimeng Luo, Young ji Lee
Recommending online resources to patients with ovarian cancer and their caregivers is a challenging task. On one hand, the recommended items must be relevant, recent, and reliable. On the other hand, they need to match the user’s levels of disease-specific health literacy. In this demonstration, we describe the overall architecture and key components of HELPeR, a knowledge-adaptive interactive recommender system for ovarian cancer patients and their caregivers.
{"title":"HELPeR: An Interactive Recommender System for Ovarian Cancer Patients and Caregivers","authors":"Behnam Rahdari, Peter Brusilovsky, Daqing He, Khushboo Thaker, Zhimeng Luo, Young ji Lee","doi":"10.1145/3523227.3551471","DOIUrl":"https://doi.org/10.1145/3523227.3551471","url":null,"abstract":"Recommending online resources to patients with ovarian cancer and their caregivers is a challenging task. On one hand, the recommended items must be relevant, recent, and reliable. On the other hand, they need to match the user’s levels of disease-specific health literacy. In this demonstration, we describe the overall architecture and key components of HELPeR, a knowledge-adaptive interactive recommender system for ovarian cancer patients and their caregivers.","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":"133121191","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}
In recommender systems, cold-start issues are situations where no previous events (e.g., ratings), are known for certain users or items. Mitigating cold-start situations is a fundamental problem in almost any recommender system [3, 5]. In real-life, large-scale production systems, the challenge of optimizing the cold-start strategy is even greater. We present an end-to-end framework for evaluating and comparing different cold-start strategies. By applying this framework in Outbrain’s recommender system, we were able to reduce our cold-start costs by half, while supporting both offline and online settings. Our framework solves the pain of benchmarking numerous cold-start techniques using surrogate accuracy metrics on offline datasets - coupled with an extensive, cost-controlled online A/B test. In this abstract, We’ll start with a short introduction to the cold-start challenge in recommender systems. Next, we will explain the motivation for a framework for cold-start techniques. Lastly, we will then describe - step by step - how we used the framework to reduce our exploration by more than 50%.
{"title":"Evaluation Framework for Cold-Start Techniques in Large-Scale Production Settings","authors":"moran haham","doi":"10.1145/3523227.3547385","DOIUrl":"https://doi.org/10.1145/3523227.3547385","url":null,"abstract":"In recommender systems, cold-start issues are situations where no previous events (e.g., ratings), are known for certain users or items. Mitigating cold-start situations is a fundamental problem in almost any recommender system [3, 5]. In real-life, large-scale production systems, the challenge of optimizing the cold-start strategy is even greater. We present an end-to-end framework for evaluating and comparing different cold-start strategies. By applying this framework in Outbrain’s recommender system, we were able to reduce our cold-start costs by half, while supporting both offline and online settings. Our framework solves the pain of benchmarking numerous cold-start techniques using surrogate accuracy metrics on offline datasets - coupled with an extensive, cost-controlled online A/B test. In this abstract, We’ll start with a short introduction to the cold-start challenge in recommender systems. Next, we will explain the motivation for a framework for cold-start techniques. Lastly, we will then describe - step by step - how we used the framework to reduce our exploration by more than 50%.","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":"129019596","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 systems are central to online information consumption and user-decision processes, as they help users find relevant information and establish new social relationships. However, recommenders could also (unintendedly) help propagate misinformation and increase the social influence of the spreading it. In this context, we study the impact of friend recommender systems on the social influence of misinformation spreaders on Twitter. To this end, we applied several user recommenders to a COVID-19 misinformation data collection. Then, we explore what-if scenarios to simulate changes in user misinformation spreading behaviour as an effect of the interactions in the recommended network. Our study shows that recommenders can indeed affect how misinformation spreaders interact with other users and influence them.
{"title":"Do Recommender Systems Make Social Media More Susceptible to Misinformation Spreaders?","authors":"Antonela Tommasel, F. Menczer","doi":"10.1145/3523227.3551473","DOIUrl":"https://doi.org/10.1145/3523227.3551473","url":null,"abstract":"Recommender systems are central to online information consumption and user-decision processes, as they help users find relevant information and establish new social relationships. However, recommenders could also (unintendedly) help propagate misinformation and increase the social influence of the spreading it. In this context, we study the impact of friend recommender systems on the social influence of misinformation spreaders on Twitter. To this end, we applied several user recommenders to a COVID-19 misinformation data collection. Then, we explore what-if scenarios to simulate changes in user misinformation spreading behaviour as an effect of the interactions in the recommended network. Our study shows that recommenders can indeed affect how misinformation spreaders interact with other users and influence them.","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":"124945252","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}
Information access systems such as search engines and recommender systems often display results in a sorted ranked list based on their relevance. Fairness of these ranked list has received attention as an important evaluation criteria along with traditional metrics such as utility or accuracy. Fairness broadly involves both provider and consumer side fairness at both group and individual levels. Several fair ranking metrics have been proposed to measure group fairness for providers based on various “sensitive attributes”. These metrics differ in their fairness goal, assumptions, and implementations. Although there are several fair ranking metrics to measure group fairness, multiple open challenges still exist in this area to consider. In my thesis, I work on the area of fair ranking metrics for provider-side group fairness. I am interested in understanding the fairness concepts and practical applications of these metrics to identify their strength and limitations to aid the researchers and practitioner by pointing out the gaps. Moreover, I will contribute to this research area by focusing on some of the limitations like considering different browsing models and bias in relevance information.
{"title":"Fair Ranking Metrics","authors":"Amifa Raj","doi":"10.1145/3523227.3547430","DOIUrl":"https://doi.org/10.1145/3523227.3547430","url":null,"abstract":"Information access systems such as search engines and recommender systems often display results in a sorted ranked list based on their relevance. Fairness of these ranked list has received attention as an important evaluation criteria along with traditional metrics such as utility or accuracy. Fairness broadly involves both provider and consumer side fairness at both group and individual levels. Several fair ranking metrics have been proposed to measure group fairness for providers based on various “sensitive attributes”. These metrics differ in their fairness goal, assumptions, and implementations. Although there are several fair ranking metrics to measure group fairness, multiple open challenges still exist in this area to consider. In my thesis, I work on the area of fair ranking metrics for provider-side group fairness. I am interested in understanding the fairness concepts and practical applications of these metrics to identify their strength and limitations to aid the researchers and practitioner by pointing out the gaps. Moreover, I will contribute to this research area by focusing on some of the limitations like considering different browsing models and bias in relevance information.","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":"114646530","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}
Himan Abdollahpouri, Shaghayegh Sherry Sahebi, Mehdi Elahi, M. Mansoury, B. Loni, Zahra Nazari, Maria Dimakopoulou
Recommender Systems are becoming an inherent part of today’s Internet. They can be found anywhere from e-commerce platforms (eBay, Amazon) to music or movie streaming (Spotify, Netflix), social media (Facebook, Instagram, TikTok), travel platforms (Booking.com, Expedia), and much more. Whether a recommendation is successful or not can rely on multiple objectives such as user satisfaction, business value, and societal issues. In addition, the long-term happiness (along with short-term excitements and delight) of the users is critical for a recommender system to be considered successful. MORS workshop brings together researchers and practitioners to discuss the importance of these aspects of recommender systems and find ways to develop algorithms to build multi-objective recommenders and also evaluation metrics to assess their success.
{"title":"MORS 2022: The Second Workshop on Multi-Objective Recommender Systems","authors":"Himan Abdollahpouri, Shaghayegh Sherry Sahebi, Mehdi Elahi, M. Mansoury, B. Loni, Zahra Nazari, Maria Dimakopoulou","doi":"10.1145/3523227.3547410","DOIUrl":"https://doi.org/10.1145/3523227.3547410","url":null,"abstract":"Recommender Systems are becoming an inherent part of today’s Internet. They can be found anywhere from e-commerce platforms (eBay, Amazon) to music or movie streaming (Spotify, Netflix), social media (Facebook, Instagram, TikTok), travel platforms (Booking.com, Expedia), and much more. Whether a recommendation is successful or not can rely on multiple objectives such as user satisfaction, business value, and societal issues. In addition, the long-term happiness (along with short-term excitements and delight) of the users is critical for a recommender system to be considered successful. MORS workshop brings together researchers and practitioners to discuss the importance of these aspects of recommender systems and find ways to develop algorithms to build multi-objective recommenders and also evaluation metrics to assess their success.","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":"115321247","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}
While personalised recommendations are successful in domains like retail, where large volumes of user feedback on items are available, the generation of automatic recommendations in data-sparse domains, like insurance purchasing, is an open problem. The insurance domain is notoriously data-sparse because the number of products is typically low (compared to retail) and they are usually purchased to last for a long time. Also, many users still prefer the telephone over the web for purchasing products, reducing the amount of web-logged user interactions. To address this, we present a recurrent neural network recommendation model that uses past user sessions as signals for learning recommendations. Learning from past user sessions allows dealing with the data scarcity of the insurance domain. Specifically, our model learns from several types of user actions that are not always associated with items, and unlike all prior session-based recommendation models, it models relationships between input sessions and a target action (purchasing insurance) that does not take place within the input sessions. Evaluation on a real-world dataset from the insurance domain (ca. 44K users, 16 items, 54K purchases, and 117K sessions) against several state-of-the-art baselines shows that our model outperforms the baselines notably. Ablation analysis shows that this is mainly due to the learning of dependencies across sessions in our model. We contribute the first ever session-based model for insurance recommendation, and make available our dataset to the research community.
{"title":"Learning Recommendations from User Actions in the Item-poor Insurance Domain","authors":"Simone Borg Bruun, Maria Maistro, C. Lioma","doi":"10.1145/3523227.3546775","DOIUrl":"https://doi.org/10.1145/3523227.3546775","url":null,"abstract":"While personalised recommendations are successful in domains like retail, where large volumes of user feedback on items are available, the generation of automatic recommendations in data-sparse domains, like insurance purchasing, is an open problem. The insurance domain is notoriously data-sparse because the number of products is typically low (compared to retail) and they are usually purchased to last for a long time. Also, many users still prefer the telephone over the web for purchasing products, reducing the amount of web-logged user interactions. To address this, we present a recurrent neural network recommendation model that uses past user sessions as signals for learning recommendations. Learning from past user sessions allows dealing with the data scarcity of the insurance domain. Specifically, our model learns from several types of user actions that are not always associated with items, and unlike all prior session-based recommendation models, it models relationships between input sessions and a target action (purchasing insurance) that does not take place within the input sessions. Evaluation on a real-world dataset from the insurance domain (ca. 44K users, 16 items, 54K purchases, and 117K sessions) against several state-of-the-art baselines shows that our model outperforms the baselines notably. Ablation analysis shows that this is mainly due to the learning of dependencies across sessions in our model. We contribute the first ever session-based model for insurance recommendation, and make available our dataset to the research community.","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":"116847302","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}
In this talk I will introduce the principles of data feminism and give a first-person report from a large participatory-action-research-design project where we are co-designing technology with data activists. The “we” in question is myself, Silvana Fumega, and Helena Suárez Val, and we work in collaboration and solidarity with activist groups producing data to challenge feminicide – fatal gender-related violence against women – across the Americas. As all practitioners know, practice is messy and rarely adheres cleanly to pleasing principles. Throughout the talk, I will highlight resonances and tensions between our design process and the principles of data feminism, showing how we tried to operationalize these principles in interactive digital tools and machine learning models. I hope to surface my aspirations for more participatory technology design processes as well as raise lingering questions to the community so that we may think together about the limitations of co-designing for justice.
{"title":"Co-designing ML Models with Data Activists","authors":"C. D’Ignazio","doi":"10.1145/3523227.3556646","DOIUrl":"https://doi.org/10.1145/3523227.3556646","url":null,"abstract":"In this talk I will introduce the principles of data feminism and give a first-person report from a large participatory-action-research-design project where we are co-designing technology with data activists. The “we” in question is myself, Silvana Fumega, and Helena Suárez Val, and we work in collaboration and solidarity with activist groups producing data to challenge feminicide – fatal gender-related violence against women – across the Americas. As all practitioners know, practice is messy and rarely adheres cleanly to pleasing principles. Throughout the talk, I will highlight resonances and tensions between our design process and the principles of data feminism, showing how we tried to operationalize these principles in interactive digital tools and machine learning models. I hope to surface my aspirations for more participatory technology design processes as well as raise lingering questions to the community so that we may think together about the limitations of co-designing for justice.","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":"117064475","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}
Though recommender systems fundamentally rely on human input and feedback, human-centered research in the RecSys discipline is lacking. When recommender systems aim to treat users more fairly, misinterpreting user objectives could lead to unintentional harm, whether or not fairness is part of the aim. When users seek to understand recommender systems better, a lack of transparency could act as an obstacle for their trust and adoption of the platform. Human-centered machine learning seeks to design systems that understand their users, while simultaneously designing systems that the users can understand. In this work, I propose to explore the intersection of transparency and user-system understanding through three phases of research that will result in a Human-Centered Framework for Transparency in Fairness-Aware Recommender Systems.
{"title":"Developing a Human-Centered Framework for Transparency in Fairness-Aware Recommender Systems","authors":"Jessie J. Smith","doi":"10.1145/3523227.3547428","DOIUrl":"https://doi.org/10.1145/3523227.3547428","url":null,"abstract":"Though recommender systems fundamentally rely on human input and feedback, human-centered research in the RecSys discipline is lacking. When recommender systems aim to treat users more fairly, misinterpreting user objectives could lead to unintentional harm, whether or not fairness is part of the aim. When users seek to understand recommender systems better, a lack of transparency could act as an obstacle for their trust and adoption of the platform. Human-centered machine learning seeks to design systems that understand their users, while simultaneously designing systems that the users can understand. In this work, I propose to explore the intersection of transparency and user-system understanding through three phases of research that will result in a Human-Centered Framework for Transparency in Fairness-Aware Recommender Systems.","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":"129521210","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}
Stitch Fix, an online personal shopping and styling service, creates a personalized shopping experience to meet any purchase occasion across multiple platforms. For example, a client who wants more one-on-one support in shopping for an outfit or look can request a stylist to curate a ‘Fix’, an assortment of 5 items; or they can browse their own personalized shop and make direct purchases in our ‘Freestyle’ experience. We know that personal style changes and evolves over time, so in order to provide the client with the most personalized and dynamic experience across platforms, it is important to recommend items based on our holistic and real-time understanding of their style across all of our platforms. This work introduces the Client Time Series Model (CTSM), a scalable and efficient recommender system based on Temporally-Masked Encoders (TME) that learns one client embedding across all platforms, yet is able to provide distinctive recommendations depending on the platform. An A/B test showed that our model outperformed the baseline model by 5.8% in terms of expected revenue.
{"title":"Client Time Series Model: a Multi-Target Recommender System based on Temporally-Masked Encoders","authors":"D. Sierag, Kevin Zielnicki","doi":"10.1145/3523227.3547397","DOIUrl":"https://doi.org/10.1145/3523227.3547397","url":null,"abstract":"Stitch Fix, an online personal shopping and styling service, creates a personalized shopping experience to meet any purchase occasion across multiple platforms. For example, a client who wants more one-on-one support in shopping for an outfit or look can request a stylist to curate a ‘Fix’, an assortment of 5 items; or they can browse their own personalized shop and make direct purchases in our ‘Freestyle’ experience. We know that personal style changes and evolves over time, so in order to provide the client with the most personalized and dynamic experience across platforms, it is important to recommend items based on our holistic and real-time understanding of their style across all of our platforms. This work introduces the Client Time Series Model (CTSM), a scalable and efficient recommender system based on Temporally-Masked Encoders (TME) that learns one client embedding across all platforms, yet is able to provide distinctive recommendations depending on the platform. An A/B test showed that our model outperformed the baseline model by 5.8% in terms of expected revenue.","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":"122690748","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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