Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval最新文献

Exploring the Spatiotemporal Features of Online Food Recommendation Service 在线美食推荐服务的时空特征探索

Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval Pub Date : 2023-07-19 DOI: 10.1145/3539618.3591853

Shaochuan Lin, Jiayan Pei, Taotao Zhou, Hengxu He, Jia Jia, Ning Hu

{"title":"Exploring the Spatiotemporal Features of Online Food Recommendation Service","authors":"Shaochuan Lin, Jiayan Pei, Taotao Zhou, Hengxu He, Jia Jia, Ning Hu","doi":"10.1145/3539618.3591853","DOIUrl":"https://doi.org/10.1145/3539618.3591853","url":null,"abstract":"Online Food Recommendation Service (OFRS) has remarkable spatiotemporal characteristics and the advantage of being able to conveniently satisfy users' needs in a timely manner. There have been a variety of studies that have begun to explore its spatiotemporal properties, but a comprehensive and in-depth analysis of the OFRS spatiotemporal features is yet to be conducted. Therefore, this paper studies the OFRS based on three questions: how spatiotemporal features play a role; why self-attention cannot be used to model the spatiotemporal sequences of OFRS; and how to combine spatiotemporal features to improve the efficiency of OFRS. Firstly, through experimental analysis, we systemically extracted the spatiotemporal features of OFRS, identified the most valuable features and designed an effective combination method. Secondly, we conducted a detailed analysis of the spatiotemporal sequences, which revealed the shortcomings of self-attention in OFRS, and proposed a more optimized spatiotemporal sequence method for replacing self-attention. In addition, we also designed a Dynamic Context Adaptation Model to further improve the efficiency and performance of OFRS. Through the offline experiments on two large datasets and online experiments for a week, the feasibility and superiority of our model were proven.","PeriodicalId":425056,"journal":{"name":"Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval","volume":"200 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133877556","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

Exploring Scenarios of Uncertainty about the Users' Preferences in Interactive Recommendation Systems 探索交互式推荐系统中用户偏好的不确定性场景

Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval Pub Date : 2023-07-19 DOI: 10.1145/3539618.3591684

N. Silva, Thiago Silva, Henrique Hott, Yan Ribeiro, A. Pereira, Leonardo Rocha

{"title":"Exploring Scenarios of Uncertainty about the Users' Preferences in Interactive Recommendation Systems","authors":"N. Silva, Thiago Silva, Henrique Hott, Yan Ribeiro, A. Pereira, Leonardo Rocha","doi":"10.1145/3539618.3591684","DOIUrl":"https://doi.org/10.1145/3539618.3591684","url":null,"abstract":"Interactive Recommender Systems have played a crucial role in distinct entertainment domains through a Contextual Bandit model. Despite the current advances, their personalisation level is still directly related to the information previously available about the users. However, there are at least two scenarios of uncertainty about the users' preferences over their journey: (1) when the user joins for the first time and (2) when the system continually makes wrong recommendations because of prior misleading assumptions. In this work, we introduce concepts from the Active Learning theory to mitigate the impact of such scenarios. We modify three traditional bandits to recommend items with a higher potential to get more user information without decreasing the model's accuracy when an uncertain scenario is observed. Our experiments show that the modified models outperform all baselines by increasing the cumulative reward in the long run. Moreover, a counterfactual evaluation validates that such improvements were not simply achieved due to the bias of offline datasets.","PeriodicalId":425056,"journal":{"name":"Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134244150","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

Offline Pseudo Relevance Feedback for Efficient and Effective Single-pass Dense Retrieval 基于离线伪相关反馈的高效单遍密集检索

Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval Pub Date : 2023-07-19 DOI: 10.1145/3539618.3592028

Xueru Wen, Xiaoyang Chen, Xuanang Chen, Ben He, Le Sun

引用次数: 0

MDKG: Graph-Based Medical Knowledge-Guided Dialogue Generation MDKG:基于图的医学知识引导对话生成

Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval Pub Date : 2023-07-19 DOI: 10.1145/3539618.3592019

Usman Naseem, Surendrabikram Thapa, Qi Zhang, Liang Hu, Mehwish Nasim

引用次数: 0

Dual Contrastive Transformer for Hierarchical Preference Modeling in Sequential Recommendation 序列推荐中分层偏好建模的双对比变压器

Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval Pub Date : 2023-07-18 DOI: 10.1145/3539618.3591672

Chengkai Huang, Shoujin Wang, Xianzhi Wang, L. Yao

{"title":"Dual Contrastive Transformer for Hierarchical Preference Modeling in Sequential Recommendation","authors":"Chengkai Huang, Shoujin Wang, Xianzhi Wang, L. Yao","doi":"10.1145/3539618.3591672","DOIUrl":"https://doi.org/10.1145/3539618.3591672","url":null,"abstract":"Sequential recommender systems (SRSs) aim to predict the subsequent items which may interest users via comprehensively modeling users' complex preference embedded in the sequence of user-item interactions. However, most of existing SRSs often model users' single low-level preference based on item ID information while ignoring the high-level preference revealed by item attribute information, such as item category. Furthermore, they often utilize limited sequence context information to predict the next item while overlooking richer inter-item semantic relations. To this end, in this paper, we proposed a novel hierarchical preference modeling framework to substantially model the complex low- and high-level preference dynamics for accurate sequential recommendation. Specifically, in the framework, a novel dual-transformer module and a novel dual contrastive learning scheme have been designed to discriminatively learn users' low- and high-level preference and to effectively enhance both low- and high-level preference learning respectively. In addition, a novel semantics-enhanced context embedding module has been devised to generate more informative context embedding for further improving the recommendation performance. Extensive experiments on six real-world datasets have demonstrated both the superiority of our proposed method over the state-of-the-art ones and the rationality of our design.","PeriodicalId":425056,"journal":{"name":"Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115420650","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

Building a Graph-Based Patent Search Engine 构建基于图形的专利搜索引擎

Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval Pub Date : 2023-07-18 DOI: 10.1145/3539618.3591842

Sebastian Björkqvist, Juho Kallio

引用次数: 0

Extending Label Aggregation Models with a Gaussian Process to Denoise Crowdsourcing Labels 用高斯过程扩展标签聚合模型去噪众包标签

Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval Pub Date : 2023-07-18 DOI: 10.1145/3539618.3591685

Dan Li, M. de Rijke

{"title":"Extending Label Aggregation Models with a Gaussian Process to Denoise Crowdsourcing Labels","authors":"Dan Li, M. de Rijke","doi":"10.1145/3539618.3591685","DOIUrl":"https://doi.org/10.1145/3539618.3591685","url":null,"abstract":"Label aggregation (LA) is the task of inferring a high-quality label for an example from multiple noisy labels generated by either human annotators or model predictions. Existing work on LA assumes a label generation process and designs a probabilistic graphical model (PGM) to learn latent true labels from observed crowd labels. However, the performance of PGM-based LA models is easily affected by the noise of the crowd labels. As a consequence, the performance of LA models differs on different datasets and no single LA model outperforms the rest on all datasets. We extend PGM-based LA models by integrating a GP prior on the true labels. The advantage of LA models extended with a GP prior is that they can take as input crowd labels, example features, and existing pre-trained label prediction models to infer the true labels, while the original LA can only leverage crowd labels. Experimental results on both synthetic and real datasets show that any LA models extended with a GP prior and a suitable mean function achieves better performance than the underlying LA models, demonstrating the effectiveness of using a GP prior.","PeriodicalId":425056,"journal":{"name":"Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval","volume":"104 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117123351","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

Tevatron: An Efficient and Flexible Toolkit for Neural Retrieval Tevatron:一个高效灵活的神经检索工具

Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval Pub Date : 2023-07-18 DOI: 10.1145/3539618.3591805

Luyu Gao

{"title":"Tevatron: An Efficient and Flexible Toolkit for Neural Retrieval","authors":"Luyu Gao","doi":"10.1145/3539618.3591805","DOIUrl":"https://doi.org/10.1145/3539618.3591805","url":null,"abstract":"Recent rapid advances in deep pre-trained language models and the introduction of large datasets have powered research in embedding-based neural retrieval. While many excellent research papers have emerged, most of them come with their own implementations, which are typically optimized for some particular research goals instead of efficiency or code organization. In this paper, we introduce Tevatron, a neural retrieval toolkit that is optimized for efficiency, flexibility, and code simplicity. Tevatron enables model training and evaluation for a variety of ranking components such as dense retrievers, sparse retrievers, and rerankers. It also provides a standardized pipeline that includes text processing, model training, corpus/query encoding, and search. In addition, Tevatron incorporates well-studied methods for improving retriever effectiveness such as hard negative mining and knowledge distillation. We provide an overview of Tevatron in this paper, demonstrating its effectiveness and efficiency on multiple IR and QA datasets. We highlight Tevatron's flexible design, which enables easy generalization across datasets, model architectures, and accelerator platforms (GPUs and TPUs). Overall, we believe that Tevatron can serve as a solid software foundation for research on neural retrieval systems, including their design, modeling, and optimization.","PeriodicalId":425056,"journal":{"name":"Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval","volume":"58 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127450405","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

Embedding Based Retrieval in Friend Recommendation 基于嵌入的好友推荐检索

Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval Pub Date : 2023-07-18 DOI: 10.1145/3539618.3591848

Jiahui Shi, Vivek Chaurasiya, Yozen Liu, Shubham Vij, Y. Wu, Satya Kanduri, Neil Shah, Peicheng Yu, Nik Srivastava, Lei Shi, Ganesh Venkataraman, Junliang Yu

{"title":"Embedding Based Retrieval in Friend Recommendation","authors":"Jiahui Shi, Vivek Chaurasiya, Yozen Liu, Shubham Vij, Y. Wu, Satya Kanduri, Neil Shah, Peicheng Yu, Nik Srivastava, Lei Shi, Ganesh Venkataraman, Junliang Yu","doi":"10.1145/3539618.3591848","DOIUrl":"https://doi.org/10.1145/3539618.3591848","url":null,"abstract":"Friend recommendation systems in online social and professional networks such as Snapchat helps users find friends and build connections, leading to better user engagement and retention. Traditional friend recommendation systems take advantage of the principle of locality and use graph traversal to retrieve friend candidates, e.g. Friends-of-Friends (FoF). While this approach has been adopted and shown efficacy in companies with large online networks such as Linkedin and Facebook, it suffers several challenges: (i) discrete graph traversal offers limited reach in cold-start settings, (ii) it is expensive and infeasible in realtime settings beyond 1 or 2 hop requests owing to latency constraints, and (iii) it cannot well-capture the complexity of graph topology or connection strengths, forcing one to resort to other mechanisms to rank and find top-K candidates. In this paper, we proposed a new Embedding Based Retrieval (EBR) system for retrieving friend candidates, which complements the traditional FoF retrieval by retrieving candidates beyond 2-hop, and providing a natural way to rank FoF candidates. Through online A/B test, we observe statistically significant improvements in the number of friendships made with EBR as an additional retrieval source in both low- and high-density network markets. Our contributions in this work include deploying a novel retrieval system to a large-scale friend recommendation system at Snapchat, generating embeddings for billions of users using Graph Neural Networks, and building EBR infrastructure in production to support Snapchat scale.","PeriodicalId":425056,"journal":{"name":"Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125114189","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

Sinkhorn Transformations for Single-Query Postprocessing in Text-Video Retrieval 文本视频检索中单查询后处理的Sinkhorn变换

Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval Pub Date : 2023-07-18 DOI: 10.1145/3539618.3592064

Konstantin Yakovlev, Gregory Polyakov, I. Alimova, A. V. Podolskiy, A. Bout, Sergey I. Nikolenko, Irina Piontkovskaya

引用次数: 0