Proceedings of the 16th ACM Conference on Recommender Systems最新文献_第9页

Streaming Session-Based Recommendation: When Graph Neural Networks meet the Neighborhood 基于流会话的推荐:当图神经网络遇到邻域时

Proceedings of the 16th ACM Conference on Recommender Systems Pub Date : 2022-09-18 DOI: 10.1145/3523227.3548485

Sara Latifi, D. Jannach

{"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}

引用次数: 4

HELPeR: An Interactive Recommender System for Ovarian Cancer Patients and Caregivers 辅助:卵巢癌患者和护理人员的互动推荐系统

Proceedings of the 16th ACM Conference on Recommender Systems Pub Date : 2022-09-18 DOI: 10.1145/3523227.3551471

Behnam Rahdari, Peter Brusilovsky, Daqing He, Khushboo Thaker, Zhimeng Luo, Young ji Lee

引用次数: 3

Evaluation Framework for Cold-Start Techniques in Large-Scale Production Settings 大规模生产环境中冷启动技术评估框架

Proceedings of the 16th ACM Conference on Recommender Systems Pub Date : 2022-09-18 DOI: 10.1145/3523227.3547385

moran haham

引用次数: 0

Do Recommender Systems Make Social Media More Susceptible to Misinformation Spreaders? 推荐系统是否使社交媒体更容易受到错误信息传播者的影响?

Proceedings of the 16th ACM Conference on Recommender Systems Pub Date : 2022-09-18 DOI: 10.1145/3523227.3551473

Antonela Tommasel, F. Menczer

引用次数: 5

Fair Ranking Metrics 公平排名指标

Proceedings of the 16th ACM Conference on Recommender Systems Pub Date : 2022-09-18 DOI: 10.1145/3523227.3547430

Amifa Raj

引用次数: 1

MORS 2022: The Second Workshop on Multi-Objective Recommender Systems MORS 2022:第二届多目标推荐系统研讨会

Proceedings of the 16th ACM Conference on Recommender Systems Pub Date : 2022-09-18 DOI: 10.1145/3523227.3547410

Himan Abdollahpouri, Shaghayegh Sherry Sahebi, Mehdi Elahi, M. Mansoury, B. Loni, Zahra Nazari, Maria Dimakopoulou

引用次数: 1

Learning Recommendations from User Actions in the Item-poor Insurance Domain 从缺少物品的保险领域的用户操作中学习建议

Proceedings of the 16th ACM Conference on Recommender Systems Pub Date : 2022-09-18 DOI: 10.1145/3523227.3546775

Simone Borg Bruun, Maria Maistro, C. Lioma

{"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}

引用次数: 3

Co-designing ML Models with Data Activists 与数据活动者共同设计ML模型

Proceedings of the 16th ACM Conference on Recommender Systems Pub Date : 2022-09-18 DOI: 10.1145/3523227.3556646

C. D’Ignazio

引用次数: 0

Developing a Human-Centered Framework for Transparency in Fairness-Aware Recommender Systems 在公平意识推荐系统中开发以人为中心的透明度框架

Proceedings of the 16th ACM Conference on Recommender Systems Pub Date : 2022-09-18 DOI: 10.1145/3523227.3547428

Jessie J. Smith

引用次数: 0

Client Time Series Model: a Multi-Target Recommender System based on Temporally-Masked Encoders 客户端时间序列模型:基于时间掩码编码器的多目标推荐系统

Proceedings of the 16th ACM Conference on Recommender Systems Pub Date : 2022-09-18 DOI: 10.1145/3523227.3547397

D. Sierag, Kevin Zielnicki

引用次数: 0