Proceedings of the 16th ACM Conference on Recommender Systems最新文献_第8页

FAccTRec 2022: The 5th Workshop on Responsible Recommendation FAccTRec 2022:第五届负责任建议研讨会

Proceedings of the 16th ACM Conference on Recommender Systems Pub Date : 2022-09-18 DOI: 10.1145/3523227.3547419

Nasim Sonboli, Toshihiro Kamishima, Amifa Raj, Luca Belli, R. Burke

引用次数: 1

Fast and Accurate User Cold-Start Learning Using Monte Carlo Tree Search 快速和准确的用户冷启动学习使用蒙特卡洛树搜索

Proceedings of the 16th ACM Conference on Recommender Systems Pub Date : 2022-09-18 DOI: 10.1145/3523227.3546786

Dilina Chandika Rajapakse, D. Leith

引用次数: 2

Query Attribute Recommendation at Amazon Search 在亚马逊搜索中查询属性推荐

Proceedings of the 16th ACM Conference on Recommender Systems Pub Date : 2022-09-18 DOI: 10.1145/3523227.3547395

Cheng-hsin Luo, William P. Headden, Neela Avudaiappan, Haoming Jiang, Tianyu Cao, Qingyu Yin, Yifan Gao, Zheng Li, R. Goutam, Haiyang Zhang, Bing Yin

引用次数: 1

Identifying New Podcasts with High General Appeal Using a Pure Exploration Infinitely-Armed Bandit Strategy 使用纯探索无限武装强盗策略识别具有高普遍吸引力的新播客

Proceedings of the 16th ACM Conference on Recommender Systems Pub Date : 2022-09-18 DOI: 10.1145/3523227.3546766

Maryam Aziz, J. Anderton, Kevin G. Jamieson, Alice Wang, Hugues Bouchard, J. Aslam

引用次数: 6

Recommendation Systems for Ad Creation: A View from the Trenches 广告创作的推荐系统:来自战壕的观点

Proceedings of the 16th ACM Conference on Recommender Systems Pub Date : 2022-09-18 DOI: 10.1145/3523227.3547401

Manisha Verma, Shaunak Mishra

{"title":"Recommendation Systems for Ad Creation: A View from the Trenches","authors":"Manisha Verma, Shaunak Mishra","doi":"10.1145/3523227.3547401","DOIUrl":"https://doi.org/10.1145/3523227.3547401","url":null,"abstract":"Creative design is one of the key components of generating engaging content on the web. E-commerce websites need engaging product descriptions, social networks require user posts to have different types of content such as videos, images and hashtags, and traditional media formats such as blogs require content creators to constantly innovate their writing style, and choice of content they publish to engage with their intended audience. Designing the right content, irrespective of the industry, is a time consuming task, often requires several iterations of content selection and modification. Advertising is one such industry where content is the key to capture user interest and generate revenue. Designing engaging and attention grabbing advertisements requires extensive domain knowledge and market trend awareness. This motivates companies to hire marketing specialists to design specific advertising content, most often tasked to create text, image or video advertisements. This process is tedious and iterative which limits the amount of content that can be produced manually. In this talk, we summarize our work focused on automating ad creative design by leveraging state of the art approaches in text mining, ranking, generation, multimodal (visual-linguistic) representations, multilingual text understanding, and recommendation. We discuss how such approaches can help to reduce the time spent on designing ads, and showcase their impact on real world advertising systems and metrics.","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":"122270505","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

Challenges in Translating Research to Practice for Evaluating Fairness and Bias in Recommendation Systems 将研究成果转化为评估推荐系统公平性和偏见的实践挑战

Proceedings of the 16th ACM Conference on Recommender Systems Pub Date : 2022-09-18 DOI: 10.1145/3523227.3547403

Lex Beattie, D. Taber, H. Cramer

引用次数: 4

Dual Attentional Higher Order Factorization Machines 双注意力高阶分解机

Proceedings of the 16th ACM Conference on Recommender Systems Pub Date : 2022-09-18 DOI: 10.1145/3523227.3546789

Arindam Sarkar, Dipankar Das, Vivek Sembium, Prakash Mandayam Comar

{"title":"Dual Attentional Higher Order Factorization Machines","authors":"Arindam Sarkar, Dipankar Das, Vivek Sembium, Prakash Mandayam Comar","doi":"10.1145/3523227.3546789","DOIUrl":"https://doi.org/10.1145/3523227.3546789","url":null,"abstract":"Numerous problems of practical significance such as clickthrough rate (CTR) prediction, forecasting, tagging and so on, involve complex interaction of various user, item and context features. Manual feature engineering has been used in the past to model these combinatorial features but it requires domain expertise and becomes prohibitively expensive as the number of features increases. Feedforward neural networks alleviate the need for manual feature engineering to a large extent and have shown impressive performance across multiple domains due to their ability to learn arbitrary functions. Despite multiple layers of non-linear projections, neural networks are limited in their ability to efficiently model functions with higher order interaction terms. In recent years, Factorization Machines and its variants have been proposed to explicitly capture higher order combinatorial interactions. However not all feature interactions are equally important, and in sparse data settings, without a suitable suppression mechanism, this might result into noisy terms during inference and hurt model generalization. In this work we present Dual Attentional Higher Order Factorization Machine (DA-HoFM), a unified attentional higher order factorization machine which leverages a compositional architecture to compute higher order terms with complexity linear in terms of maximum interaction degree. Equipped with sparse dual attention mechanism, DA-HoFM summarizes interaction terms at each layer, and is able to efficiently select important higher order terms. We empirically demonstrate effectiveness of our proposed models on the task of CTR prediction, where our model exhibits superior performance compared to the recent state-of-the-art models, outperforming them by up to 6.7% on the logloss metric.","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":"116768722","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

TinyKG: Memory-Efficient Training Framework for Knowledge Graph Neural Recommender Systems TinyKG:知识图神经推荐系统的记忆效率训练框架

Proceedings of the 16th ACM Conference on Recommender Systems Pub Date : 2022-09-18 DOI: 10.1145/3523227.3546760

Huiyuan Chen, Xiaoting Li, Kaixiong Zhou, Xia Hu, Chin-Chia Michael Yeh, Yan Zheng, Hao Yang

{"title":"TinyKG: Memory-Efficient Training Framework for Knowledge Graph Neural Recommender Systems","authors":"Huiyuan Chen, Xiaoting Li, Kaixiong Zhou, Xia Hu, Chin-Chia Michael Yeh, Yan Zheng, Hao Yang","doi":"10.1145/3523227.3546760","DOIUrl":"https://doi.org/10.1145/3523227.3546760","url":null,"abstract":"There has been an explosion of interest in designing various Knowledge Graph Neural Networks (KGNNs), which achieve state-of-the-art performance and provide great explainability for recommendation. The promising performance is mainly resulting from their capability of capturing high-order proximity messages over the knowledge graphs. However, training KGNNs at scale is challenging due to the high memory usage. In the forward pass, the automatic differentiation engines (e.g., TensorFlow/PyTorch) generally need to cache all intermediate activation maps in order to compute gradients in the backward pass, which leads to a large GPU memory footprint. Existing work solves this problem by utilizing multi-GPU distributed frameworks. Nonetheless, this poses a practical challenge when seeking to deploy KGNNs in memory-constrained environments, especially for industry-scale graphs. Here we present TinyKG, a memory-efficient GPU-based training framework for KGNNs for the tasks of recommendation. Specifically, TinyKG uses exact activations in the forward pass while storing a quantized version of activations in the GPU buffers. During the backward pass, these low-precision activations are dequantized back to full-precision tensors, in order to compute gradients. To reduce the quantization errors, TinyKG applies a simple yet effective quantization algorithm to compress the activations, which ensures unbiasedness with low variance. As such, the training memory footprint of KGNNs is largely reduced with negligible accuracy loss. To evaluate the performance of our TinyKG, we conduct comprehensive experiments on real-world datasets. We found that our TinyKG with INT2 quantization aggressively reduces the memory footprint of activation maps with 7 ×, only with 2% loss in accuracy, allowing us to deploy KGNNs on memory-constrained devices.","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":"130327199","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

Optimizing product recommendations for millions of merchants 为数百万商家优化产品推荐

Proceedings of the 16th ACM Conference on Recommender Systems Pub Date : 2022-09-18 DOI: 10.1145/3523227.3547393

Kim Falk, Chen Karako

引用次数: 1

Training and Deploying Multi-Stage Recommender Systems 培训和部署多阶段推荐系统

Proceedings of the 16th ACM Conference on Recommender Systems Pub Date : 2022-09-18 DOI: 10.1145/3523227.3547372

Ronay Ak, Benedikt D. Schifferer, Sara Rabhi, G. Moreira

{"title":"Training and Deploying Multi-Stage Recommender Systems","authors":"Ronay Ak, Benedikt D. Schifferer, Sara Rabhi, G. Moreira","doi":"10.1145/3523227.3547372","DOIUrl":"https://doi.org/10.1145/3523227.3547372","url":null,"abstract":"Industrial recommender systems are made up of complex pipelines requiring multiple steps including feature engineering and preprocessing, a retrieval model for candidate generation, filtering, a feature store query, a ranking model for scoring, and an ordering stage. These pipelines need to be carefully deployed as a set, requiring coordination during their development and deployment. Data scientists, ML engineers, and researchers might focus on different stages of recommender systems, however they share a common desire to reduce the time and effort searching for and combining boilerplate code coming from different sources or writing custom code from scratch to create their own RecSys pipelines. This tutorial introduces the Merlin framework which aims to make the development and deployment of recommender systems easier, providing methods for evaluating existing approaches, developing new ideas and deploying them to production. There are many techniques, such as different model architectures (e.g. MF, DLRM, DCN, etc), negative sampling strategies, loss functions or prediction tasks (binary, multi-class, multi-task) that are commonly used in these pipelines. Merlin provides building blocks that allow RecSys practitioners to focus on the “what” question in designing their model pipeline instead of “how”. Supporting research into new ideas within the RecSys spaces is equally important and Merlin supports the addition of custom components and the extension of existing ones to address gaps. In this tutorial, participants will learn: (i) how to easily implement common recommender system techniques for comparison, (ii) how to modify components to evaluate new ideas, and (iii) deploying recommender systems, bringing new ideas to production- using an open source framework Merlin and its libraries.","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":"124962645","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