Proceedings of the 16th ACM Conference on Recommender Systems最新文献_第2页

Designing and evaluating explainable AI for non-AI experts: challenges and opportunities 为非AI专家设计和评估可解释的AI:挑战和机遇

Proceedings of the 16th ACM Conference on Recommender Systems Pub Date : 2022-09-18 DOI: 10.1145/3523227.3547427

Maxwell Szymanski, K. Verbert, V. Abeele

引用次数: 3

A GPU-specialized Inference Parameter Server for Large-Scale Deep Recommendation Models 面向大规模深度推荐模型的gpu专用推理参数服务器

Proceedings of the 16th ACM Conference on Recommender Systems Pub Date : 2022-09-18 DOI: 10.1145/3523227.3546765

Yingcan Wei, Matthias Langer, F. Yu, Minseok Lee, Jie Liu, Ji Shi, Zehuan Wang

{"title":"A GPU-specialized Inference Parameter Server for Large-Scale Deep Recommendation Models","authors":"Yingcan Wei, Matthias Langer, F. Yu, Minseok Lee, Jie Liu, Ji Shi, Zehuan Wang","doi":"10.1145/3523227.3546765","DOIUrl":"https://doi.org/10.1145/3523227.3546765","url":null,"abstract":"Recommendation systems are of crucial importance for a variety of modern apps and web services, such as news feeds, social networks, e-commerce, search, etc. To achieve peak prediction accuracy, modern recommendation models combine deep learning with terabyte-scale embedding tables to obtain a fine-grained representation of the underlying data. Traditional inference serving architectures require deploying the whole model to standalone servers, which is infeasible at such massive scale. In this paper, we provide insights into the intriguing and challenging inference domain of online recommendation systems. We propose the HugeCTR Hierarchical Parameter Server (HPS), an industry-leading distributed recommendation inference framework, that combines a high-performance GPU embedding cache with an hierarchical storage architecture, to realize low-latency retrieval of embeddings for online model inference tasks. Among other things, HPS features (1) a redundant hierarchical storage system, (2) a novel high-bandwidth cache to accelerate parallel embedding lookup on NVIDIA GPUs, (3) online training support and (4) light-weight APIs for easy integration into existing large-scale recommendation workflows. To demonstrate its capabilities, we conduct extensive studies using both synthetically engineered and public datasets. We show that our HPS can dramatically reduce end-to-end inference latency, achieving 5~62x speedup (depending on the batch size) over CPU baseline implementations for popular recommendation models. Through multi-GPU concurrent deployment, the HPS can also greatly increase the inference QPS.","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":"128241467","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

Countering Popularity Bias by Regularizing Score Differences 通过规范分数差异来对抗人气偏见

Proceedings of the 16th ACM Conference on Recommender Systems Pub Date : 2022-09-18 DOI: 10.1145/3523227.3546757

Wondo Rhee, S. Cho, B. Suh

{"title":"Countering Popularity Bias by Regularizing Score Differences","authors":"Wondo Rhee, S. Cho, B. Suh","doi":"10.1145/3523227.3546757","DOIUrl":"https://doi.org/10.1145/3523227.3546757","url":null,"abstract":"Recommendation system often suffers from popularity bias. Often the training data inherently exhibits long-tail distribution in item popularity (data bias). Moreover, the recommendation systems could give unfairly higher recommendation scores to popular items even among items a user equally liked, resulting in over-recommendation of popular items (model bias). In this study we propose a novel method to reduce the model bias while maintaining accuracy by directly regularizing the recommendation scores to be equal across items a user preferred. Akin to contrastive learning, we extend the widely used pairwise loss (BPR loss) which maximizes the score differences between preferred and unpreferred items, with a regularization term that minimizes the score differences within preferred and unpreferred items, respectively, thereby achieving both high debias and high accuracy performance with no additional training. To test the effectiveness of the proposed method, we design an experiment using a synthetic dataset which induces model bias with baseline training; we showed applying the proposed method resulted in drastic reduction of model bias while maintaining accuracy. Comprehensive comparison with earlier debias methods showed the proposed method had advantages in terms of computational validity and efficiency. Further empirical experiments utilizing four benchmark datasets and four recommendation models indicated the proposed method showed general improvements over performances of earlier debias methods. We hope that our method could help users enjoy diverse recommendations promoting serendipitous findings. Code available at https://github.com/stillpsy/popbias.","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":"127925969","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

Neural Re-ranking for Multi-stage Recommender Systems 多阶段推荐系统的神经重排序

Proceedings of the 16th ACM Conference on Recommender Systems Pub Date : 2022-09-18 DOI: 10.1145/3523227.3547369

Weiwen Liu, Jiarui Qin, Ruiming Tang, Bo Chen

引用次数: 1

TorchRec: a PyTorch Domain Library for Recommendation Systems TorchRec:一个推荐系统的PyTorch领域库

Proceedings of the 16th ACM Conference on Recommender Systems Pub Date : 2022-09-18 DOI: 10.1145/3523227.3547387

Dmytro Ivchenko, D. V. Staay, Colin Taylor, Xing Liu, Will Feng, Rahul Kindi, Anirudh Sudarshan, S. Sefati

{"title":"TorchRec: a PyTorch Domain Library for Recommendation Systems","authors":"Dmytro Ivchenko, D. V. Staay, Colin Taylor, Xing Liu, Will Feng, Rahul Kindi, Anirudh Sudarshan, S. Sefati","doi":"10.1145/3523227.3547387","DOIUrl":"https://doi.org/10.1145/3523227.3547387","url":null,"abstract":"Recommendation Systems (RecSys) comprise a large footprint of production-deployed AI today. The neural network-based recommender systems differ from deep learning models in other domains in using high-cardinality categorical sparse features that require large embedding tables to be trained. In this talk we introduce TorchRec, a PyTorch domain library for Recommendation Systems. This new library provides common sparsity and parallelism primitives, enabling researchers to build state-of-the-art personalization models and deploy them in production. In this talk we cover the building blocks of the TorchRec library including modeling primitives such as embedding bags and jagged tensors, optimized recommender system kernels powered by FBGEMM, a flexible sharder that supports a veriety of strategies for partitioning embedding tables, a planner that automatically generates optimized and performant sharding plans, support for GPU inference and common modeling modules for building recommender system models. TorchRec library is currently used to train large-scale recommender models at Meta. We will present how TorchRec helped Meta’s recommender system platform to transition from CPU asynchronous training to accelerator-based full-sync training.","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":"134131939","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: 8

Heterogeneous Graph Representation Learning for multi-target Cross-Domain Recommendation 面向多目标跨领域推荐的异构图表示学习

Proceedings of the 16th ACM Conference on Recommender Systems Pub Date : 2022-09-18 DOI: 10.1145/3523227.3547426

Tendai Mukande

引用次数: 0

Imbalanced Data Sparsity as a Source of Unfair Bias in Collaborative Filtering 协同过滤中不公平偏差的来源——不平衡数据稀疏性

Proceedings of the 16th ACM Conference on Recommender Systems Pub Date : 2022-09-18 DOI: 10.1145/3523227.3547404

Aditya Joshi, Chin Lin Wong, Diego Marinho de Oliveira, Farhad Zafari, Fernando Mourão, Sabir Ribas, Saumya Pandey

{"title":"Imbalanced Data Sparsity as a Source of Unfair Bias in Collaborative Filtering","authors":"Aditya Joshi, Chin Lin Wong, Diego Marinho de Oliveira, Farhad Zafari, Fernando Mourão, Sabir Ribas, Saumya Pandey","doi":"10.1145/3523227.3547404","DOIUrl":"https://doi.org/10.1145/3523227.3547404","url":null,"abstract":"Collaborative Filtering (CF) is a class of methods widely used to support high-quality Recommender Systems (RSs) across several industries [6]. Studies have uncovered distinct advantages and limitations of CF in many real-world applications [5, 9]. Besides the inability to address the cold-start problem, sensitivity to data sparsity is among the main limitations recurrently associated with this class of RSs. Past work has extensively demonstrated that data sparsity critically impacts CF accuracy [2, 3, 4]. The proposed talk revisits the relation between data sparsity and CF from a new perspective, evincing that the former also impacts the fairness of recommendations. In particular, data sparsity might lead to unfair bias in domains where the volume of activity strongly correlates with personal characteristics that are protected by law (i.e., protected attributes). This concern is critical for RSs deployed in domains such as the recruitment domain, where RSs have been reported to automate or facilitate discriminatory behaviour [7]. Our work at SEEK deals with recommender algorithms that recommend jobs to candidates via SEEK’s multiple channels. While this talk focuses on our perspective of the problem in the job recommendation domain, the discussion is relevant to many other domains where recommenders potentially have a social or economic impact on the lives of individuals and groups.","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":"114038795","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

An Interpretable Neural Network Model for Bundle Recommendations: Doctoral Symposium, Extended Abstract 束推荐的可解释神经网络模型:博士研讨会，扩展摘要

Proceedings of the 16th ACM Conference on Recommender Systems Pub Date : 2022-09-18 DOI: 10.1145/3523227.3547423

Xinyi Li, E. Malthouse

引用次数: 0

A Multi-Stakeholder Recommender System for Rewards Recommendations 奖励推荐的多利益相关者推荐系统

Proceedings of the 16th ACM Conference on Recommender Systems Pub Date : 2022-09-18 DOI: 10.1145/3523227.3547388

Naime Ranjbar Kermany, L. Pizzato, Thireindar Min, Callum Scott, A. Leontjeva

引用次数: 1

Fourth Knowledge-aware and Conversational Recommender Systems Workshop (KaRS) 第四届知识感知与会话推荐系统研讨会

Proceedings of the 16th ACM Conference on Recommender Systems Pub Date : 2022-09-18 DOI: 10.1145/3523227.3547412

V. W. Anelli, Pierpaolo Basile, Gerard de Melo, F. Donini, Antonio Ferrara, C. Musto, F. Narducci, A. Ragone, M. Zanker

{"title":"Fourth Knowledge-aware and Conversational Recommender Systems Workshop (KaRS)","authors":"V. W. Anelli, Pierpaolo Basile, Gerard de Melo, F. Donini, Antonio Ferrara, C. Musto, F. Narducci, A. Ragone, M. Zanker","doi":"10.1145/3523227.3547412","DOIUrl":"https://doi.org/10.1145/3523227.3547412","url":null,"abstract":"In the last few years, a renewed interest of the research community in conversational recommender systems (CRSs) has been emerging. This is likely due to the massive proliferation of Digital Assistants (DAs) such as Amazon Alexa, Siri, or Google Assistant that are revolutionizing the way users interact with machines. DAs allow users to execute a wide range of actions through an interaction mostly based on natural language utterances. However, although DAs are able to complete tasks such as sending texts, making phone calls, or playing songs, they still remain at an early stage in terms of their recommendation capabilities via a conversation. In addition, we have been witnessing the advent of increasingly precise and powerful recommendation algorithms and techniques able to effectively assess users’ tastes and predict information that may be of interest to them. Most of these approaches rely on the collaborative paradigm (often exploiting machine learning techniques) and neglect the huge amount of knowledge, both structured and unstructured, describing the domain of interest of a recommendation engine. Although very effective in predicting relevant items, collaborative approaches miss some very interesting features that go beyond the accuracy of results and move in the direction of providing novel and diverse results as well as generating explanations for recommended items. Knowledge-aware side information becomes crucial when a conversational interaction is implemented, in particular for preference elicitation, explanation, and critiquing steps.","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":"115834856","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