Proceedings of the 16th ACM Conference on Recommender Systems最新文献_第7页

Recommending for a multi-sided marketplace with heterogeneous contents 推荐一个具有异构内容的多边市场

Proceedings of the 16th ACM Conference on Recommender Systems Pub Date : 2022-09-18 DOI: 10.1145/3523227.3547379

Yuyan Wang, Long Tao, Xian-Xing Zhang

{"title":"Recommending for a multi-sided marketplace with heterogeneous contents","authors":"Yuyan Wang, Long Tao, Xian-Xing Zhang","doi":"10.1145/3523227.3547379","DOIUrl":"https://doi.org/10.1145/3523227.3547379","url":null,"abstract":"Many online personalization platforms today are recommending heterogeneous contents in a multi-sided marketplace consisting of consumers, merchants and other partners. For a recommender system to be successful in these contexts, it faces two main challenges. First, each side in the marketplace has different and potentially conflicting utilities. Recommending for a multi-sided marketplace therefore entails jointly optimizing multiple objectives with trade-offs. Second, the off-the-shelf recommendation algorithms are not applicable to the heterogeneous content space, where a recommendation item could be an aggregation of other recommendation items. In this work, we develop a general framework for recommender systems in a multi-sided marketplace with heterogeneous and hierarchical contents. We propose a constrained optimization framework with machine learning models for each objective as inputs, and a probabilistic structural model for users’ engagement patterns on heterogeneous contents. Our proposed structural modeling approach ensures consistent user experience across different levels of aggregation of the contents, and provides levels of transparency to the merchants and content providers. We further develop an efficient optimization solution for ranking and recommendation in large-scale online systems in real time. We implement the framework at Uber Eats, one of the largest online food delivery platforms in the world and a three-sided marketplace consisting of eaters, restaurant partners and delivery partners. Online experiments demonstrate the effectiveness of our framework in ranking heterogeneous contents and optimizing for the three sides in the marketplace. Our framework has been deployed globally as the recommendation algorithm for Uber Eats’ homepage.","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":"132340870","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

M2TRec: Metadata-aware Multi-task Transformer for Large-scale and Cold-start free Session-based Recommendations M2TRec:用于大规模和冷启动免费会话的元数据感知多任务转换器

Proceedings of the 16th ACM Conference on Recommender Systems Pub Date : 2022-09-18 DOI: 10.1145/3523227.3551477

W. Shalaby, Sejoon Oh, Amir Afsharinejad, Srijan Kumar, Xiquan Cui

{"title":"M2TRec: Metadata-aware Multi-task Transformer for Large-scale and Cold-start free Session-based Recommendations","authors":"W. Shalaby, Sejoon Oh, Amir Afsharinejad, Srijan Kumar, Xiquan Cui","doi":"10.1145/3523227.3551477","DOIUrl":"https://doi.org/10.1145/3523227.3551477","url":null,"abstract":"Session-based recommender systems (SBRSs) have shown superior performance over conventional methods. However, they show limited scalability on large-scale industrial datasets since most models learn one embedding per item. This leads to a large memory requirement (of storing one vector per item) and poor performance on sparse sessions with cold-start or unpopular items. Using one public and one large industrial dataset, we experimentally show that state-of-the-art SBRSs have low performance on sparse sessions with sparse items. We propose M2TRec, a Metadata-aware Multi-task Transformer model for session-based recommendations. Our proposed method learns a transformation function from item metadata to embeddings, and is thus, item-ID free (i.e., does not need to learn one embedding per item). It integrates item metadata to learn shared representations of diverse item attributes. During inference, new or unpopular items will be assigned identical representations for the attributes they share with items previously observed during training, and thus will have similar representations with those items, enabling recommendations of even cold-start and sparse items. Additionally, M2TRec is trained in a multi-task setting to predict the next item in the session along with its primary category and subcategories. Our multi-task strategy makes the model converge faster and significantly improves the overall performance. Experimental results show significant performance gains using our proposed approach on sparse items on the two datasets.","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":"133162867","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: 10

Merlin HugeCTR: GPU-accelerated Recommender System Training and Inference 梅林HugeCTR: gpu加速推荐系统的训练和推理

Proceedings of the 16th ACM Conference on Recommender Systems Pub Date : 2022-09-18 DOI: 10.1145/3523227.3547405

Zehuan Wang, Yingcan Wei, Minseok Lee, Matthias Langer, F. Yu, Jie Liu, Shijie Liu, Daniel G. Abel, Xu Guo, Jianbing Dong, Ji Shi, Kunlun Li

{"title":"Merlin HugeCTR: GPU-accelerated Recommender System Training and Inference","authors":"Zehuan Wang, Yingcan Wei, Minseok Lee, Matthias Langer, F. Yu, Jie Liu, Shijie Liu, Daniel G. Abel, Xu Guo, Jianbing Dong, Ji Shi, Kunlun Li","doi":"10.1145/3523227.3547405","DOIUrl":"https://doi.org/10.1145/3523227.3547405","url":null,"abstract":"In this talk, we introduce Merlin HugeCTR. Merlin HugeCTR is an open source, GPU-accelerated integration framework for click-through rate estimation. It optimizes both training and inference, whilst enabling model training at scale with model-parallel embeddings and data-parallel neural networks. In particular, Merlin HugeCTR combines a high-performance GPU embedding cache with an hierarchical storage architecture, to realize low-latency retrieval of embeddings for online model inference tasks. In the MLPerf v1.0 DLRM model training benchmark, Merlin HugeCTR achieves a speedup of up to 24.6x on a single DGX A100 (8x A100) over PyTorch on 4x4-socket CPU nodes (4x4x28 cores). Merlin HugeCTR can also take advantage of multi-node environments to accelerate training even further. Since late 2021, Merlin HugeCTR additionally features a hierarchical parameter server (HPS) and supports deployment via the NVIDIA Triton server framework, to leverage the computational capabilities of GPUs for high-speed recommendation model inference. Using this HPS, Merlin HugeCTR users can achieve a 5~62x speedup (batch size dependent) for popular recommendation models over CPU baseline implementations, and dramatically reduce their end-to-end inference latency.","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":"132790792","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: 12

Knowledge-aware Recommendations Based on Neuro-Symbolic Graph Embeddings and First-Order Logical Rules 基于神经符号图嵌入和一阶逻辑规则的知识感知推荐

Proceedings of the 16th ACM Conference on Recommender Systems Pub Date : 2022-09-18 DOI: 10.1145/3523227.3551484

Giuseppe Spillo, C. Musto, M. Degemmis, P. Lops, G. Semeraro

引用次数: 9

“My AI must have been broken”: How AI Stands to Reshape Human Communication “我的人工智能肯定坏了”:人工智能如何重塑人类交流

Proceedings of the 16th ACM Conference on Recommender Systems Pub Date : 2022-09-18 DOI: 10.1145/3523227.3555724

Mor Naaman

{"title":"“My AI must have been broken”: How AI Stands to Reshape Human Communication","authors":"Mor Naaman","doi":"10.1145/3523227.3555724","DOIUrl":"https://doi.org/10.1145/3523227.3555724","url":null,"abstract":"From autocomplete and smart replies to video filters and deepfakes, we increasingly live in a world where communication between humans is augmented by artificial intelligence. AI often operates on behalf of a human communicator by recommending, suggesting, modifying, or generating messages to accomplish communication goals. We call this phenomenon AI-Mediated Communication (or AI-MC) [1, 4]. While AI-MC has the potential of making human communication more efficient, it impacts other aspects of our communication in ways that are not yet well understood. Over the last three years, my collaborators and I have been documenting the impact of AI-MC on communication outcomes, language use, interpersonal trust, and more. The talk will outline early experimental findings from this work, mostly led by Cornell and Stanford graduate students Maurice Jakesch, Hannah Mieczkowski, and Jess Hohenstein. For example, the research shows that AI-MC involvement can result in language shifting towards positivity [2, 7]; impact the evaluation of others [2, 4]; change the extent to which we take ownership over our messages [6]; and shift assignment of blame for communication outcomes [3]. Given the impact of AI-MC on interpersonal evaluations, the talk will also cover our recent research examining the (mostly false) heuristics humans use when evaluating whether text was written by AI [5]. Overall, AI-MC raises significant practical and ethical concerns as it stands to reshape human communication, calling for new approaches to the development and regulation of these technologies.","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":"132551047","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

Tutorial on Offline Evaluation for Group Recommender Systems 小组推荐系统的离线评估教程

Proceedings of the 16th ACM Conference on Recommender Systems Pub Date : 2022-09-18 DOI: 10.1145/3523227.3547371

F. Barile, Amra Delic, Ladislav Peška

引用次数: 3

Solving Diversity-Aware Maximum Inner Product Search Efficiently and Effectively 高效地求解多样性感知的最大内积搜索

Proceedings of the 16th ACM Conference on Recommender Systems Pub Date : 2022-09-18 DOI: 10.1145/3523227.3546779

Kohei Hirata, Daichi Amagata, Sumio Fujita, Takahiro Hara

{"title":"Solving Diversity-Aware Maximum Inner Product Search Efficiently and Effectively","authors":"Kohei Hirata, Daichi Amagata, Sumio Fujita, Takahiro Hara","doi":"10.1145/3523227.3546779","DOIUrl":"https://doi.org/10.1145/3523227.3546779","url":null,"abstract":"Maximum inner product search (or k-MIPS) is a fundamental operation in recommender systems that infer preferable items for users. To support large-scale recommender systems, existing studies designed scalable k-MIPS algorithms. However, these studies do not consider diversity, although recommending diverse items is important to improve user satisfaction. We therefore formulate a new problem, namely diversity-aware k-MIPS. In this problem, users can control the degree of diversity in their recommendation lists through a parameter. However, exactly solving this problem is unfortunately NP-hard, so it is challenging to devise an efficient, effective, and practical algorithm for the diversity-aware k-MIPS problem. This paper overcomes this challenge and proposes IP-Greedy, which incorporates new early termination and skipping techniques into a greedy algorithm. We conduct extensive experiments on real datasets, and the results demonstrate the efficiency and effectiveness of our algorithm. Also, we conduct a case study of the diversity-aware k-MIPS problem on a real dataset. We confirm that this problem can make recommendation lists diverse while preserving high inner products of user and item vectors in the lists.","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":"125243562","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: 9

Fourth Workshop on Recommender Systems in Fashion and Retail – fashionXrecsys2022 第四届时尚与零售推荐系统研讨会- fashionXrecsys2022

Proceedings of the 16th ACM Conference on Recommender Systems Pub Date : 2022-09-18 DOI: 10.1145/3523227.3547417

Reza Shirvany, Humberto Jesús Corona Pampín

引用次数: 0

RecPack: An(other) Experimentation Toolkit for Top-N Recommendation using Implicit Feedback Data RecPack:使用隐式反馈数据进行Top-N推荐的(另一个)实验工具包

Proceedings of the 16th ACM Conference on Recommender Systems Pub Date : 2022-09-18 DOI: 10.1145/3523227.3551472

L. Michiels, Robin Verachtert, Bart Goethals

引用次数: 8

Towards the Evaluation of Recommender Systems with Impressions 基于印象的推荐系统评价研究

Proceedings of the 16th ACM Conference on Recommender Systems Pub Date : 2022-09-18 DOI: 10.1145/3523227.3551483

Fernando Benjamín Pérez Maurera, Maurizio Ferrari Dacrema, P. Cremonesi

{"title":"Towards the Evaluation of Recommender Systems with Impressions","authors":"Fernando Benjamín Pérez Maurera, Maurizio Ferrari Dacrema, P. Cremonesi","doi":"10.1145/3523227.3551483","DOIUrl":"https://doi.org/10.1145/3523227.3551483","url":null,"abstract":"In Recommender Systems, impressions are a relatively new type of information that records all products previously shown to the users. They are also a complex source of information, combining the effects of the recommender system that generated them, search results, or business rules that may select specific products for recommendations. The fact that the user interacted with a specific item given a list of recommended ones may benefit from a richer interaction signal, in which some items the user did not interact with may be considered negative interactions. This work presents a preliminary evaluation of recommendation models with impressions. First, impressions are characterized by describing their assumptions, signals, and challenges. Then, an evaluation study with impressions is described. The study’s goal is two-fold: to measure the effects of impressions data on properly-tuned recommendation models using current open-source datasets and disentangle the signals within impressions data. Preliminary results suggest that impressions data and signals are nuanced, complex, and effective at improving the recommendation quality of recommenders. This work publishes the source code, datasets, and scripts used in the evaluation to promote reproducibility in the domain.","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":"121803698","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: 6