Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval最新文献_第9页

PEPO: Petition Executing Processing Optimizer Based on Natural Language Processing 基于自然语言处理的请愿执行处理优化器

Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval Pub Date : 2023-07-18 DOI: 10.1145/3539618.3591811

Yin-Wei Chiu, Hsiao-Ching Huang, Cheng-Ju Lee, Hsun-Ping Hsieh

引用次数: 0

Distributionally Robust Sequential Recommnedation 分布式鲁棒顺序推荐

Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval Pub Date : 2023-07-18 DOI: 10.1145/3539618.3591668

Rui Zhou, X. Wu, Zhaopeng Qiu, Yefeng Zheng, Xu Chen

{"title":"Distributionally Robust Sequential Recommnedation","authors":"Rui Zhou, X. Wu, Zhaopeng Qiu, Yefeng Zheng, Xu Chen","doi":"10.1145/3539618.3591668","DOIUrl":"https://doi.org/10.1145/3539618.3591668","url":null,"abstract":"Modeling user sequential behaviors have been demonstrated to be effective in promoting the recommendation performance. While previous work has achieved remarkable successes, they mostly assume that the training and testing distributions are consistent, which may contradict with the diverse and complex user preferences, and limit the recommendation performance in real-world scenarios. To alleviate this problem, in this paper, we propose a robust sequential recommender framework to overcome the potential distribution shift between the training and testing sets. In specific, we firstly simulate different training distributions via sample reweighting. Then, we minimize the largest loss induced by these distributions to optimize the 'worst-case' loss for improving the model robustness. Considering that there can be too many sample weights, which may introduce too much flexibility and be hard to optimize, we cluster the training samples based on both hard and soft strategies, and assign each cluster with a unified weight. At last, we analyze our framework by presenting the generalization error bound of the above minimax objective, which help us to better understand the proposed framework from the theoretical perspective. We conduct extensive experiments based on three real-world datasets to demonstrate the effectiveness of our proposed framework. To reproduce our experiments and promote this research direction, we have released our project at https://anonymousrsr.github.io/RSR/.","PeriodicalId":425056,"journal":{"name":"Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval","volume":"1 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":"132640860","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

DeviceGPT: A Generative Pre-Training Transformer on the Heterogenous Graph for Internet of Things DeviceGPT:物联网异构图上的生成式预训练转换器

Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval Pub Date : 2023-07-18 DOI: 10.1145/3539618.3591972

Yimo Ren, Jinfang Wang, Hong Li, Hongsong Zhu, Limin Sun

引用次数: 0

Multi-grained Representation Learning for Cross-modal Retrieval 跨模态检索的多粒度表示学习

Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval Pub Date : 2023-07-18 DOI: 10.1145/3539618.3592025

Shengwei Zhao, Linhai Xu, Yuying Liu, S. Du

{"title":"Multi-grained Representation Learning for Cross-modal Retrieval","authors":"Shengwei Zhao, Linhai Xu, Yuying Liu, S. Du","doi":"10.1145/3539618.3592025","DOIUrl":"https://doi.org/10.1145/3539618.3592025","url":null,"abstract":"The purpose of audio-text retrieval is to learn a cross-modal similarity function between audio and text, enabling a given audio/text to find similar text/audio from a candidate set. Recent audio-text retrieval models aggregate multi-modal features into a single-grained representation. However, single-grained representation is difficult to solve the situation that an audio is described by multiple texts of different granularity levels, because the association pattern between audio and text is complex. Therefore, we propose an adaptive aggregation strategy to automatically find the optimal pool function to aggregate the features into a comprehensive representation, so as to learn valuable multi-grained representation. And multi-grained comparative learning is carried out in order to focus on the complex correlation between audio and text in different granularity. Meanwhile, text-guided token interaction is used to reduce the impact of redundant audio clips. We evaluated our proposed method on two audio-text retrieval benchmark datasets of Audiocaps and Clotho, achieving the state-of-the-art results in text-to-audio and audio-to-text retrieval. Our findings emphasize the importance of learning multi-modal multi-grained representation.","PeriodicalId":425056,"journal":{"name":"Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval","volume":"238 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":"131588304","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

Quantifying Ranker Coverage of Different Query Subspaces 量化不同查询子空间的rank覆盖率

Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval Pub Date : 2023-07-18 DOI: 10.1145/3539618.3592045

Negar Arabzadeh, A. Bigdeli, Radin Hamidi Rad, E. Bagheri

{"title":"Quantifying Ranker Coverage of Different Query Subspaces","authors":"Negar Arabzadeh, A. Bigdeli, Radin Hamidi Rad, E. Bagheri","doi":"10.1145/3539618.3592045","DOIUrl":"https://doi.org/10.1145/3539618.3592045","url":null,"abstract":"The information retrieval community has observed significant performance improvements over various tasks due to the introduction of neural architectures. However, such improvements do not necessarily seem to have happened uniformly across a range of queries. As we will empirically show in this paper, the performance of neural rankers follow a long-tail distribution where there are many subsets of queries, which are not effectively satisfied by neural methods. Despite this observation, performance is often reported using standard retrieval metrics, such as MRR or nDCG, which capture average performance over all queries. As such, it is not clear whether reported improvements are due to incremental boost on a small subset of already well-performing queries or addressing queries that have been difficult to address by existing methods. In this paper, we propose the Task Subspace Coverage (TaSC /tAHsk/) metric, which systematically quantifies whether and to what extent improvements in retrieval effectiveness happen on similar or disparate query subspaces for different rankers. Our experiments show that the consideration of our proposed TaSC metric in conjunction with existing ranking metrics provides deeper insight into ranker performance and their contribution to overall advances on a given task.","PeriodicalId":425056,"journal":{"name":"Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval","volume":"80 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":"127606538","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

AutoTransfer: Instance Transfer for Cross-Domain Recommendations AutoTransfer:跨域建议的实例传输

Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval Pub Date : 2023-07-18 DOI: 10.1145/3539618.3591701

Jingtong Gao, Xiangyu Zhao, Bo Chen, Fan Yan, Huifeng Guo, Ruiming Tang

{"title":"AutoTransfer: Instance Transfer for Cross-Domain Recommendations","authors":"Jingtong Gao, Xiangyu Zhao, Bo Chen, Fan Yan, Huifeng Guo, Ruiming Tang","doi":"10.1145/3539618.3591701","DOIUrl":"https://doi.org/10.1145/3539618.3591701","url":null,"abstract":"Cross-Domain Recommendation (CDR) is a widely used approach for leveraging information from domains with rich data to assist domains with insufficient data. A key challenge of CDR research is the effective and efficient transfer of helpful information from source domain to target domain. Currently, most existing CDR methods focus on extracting implicit information from the source domain to enhance the target domain. However, the hidden structure of the extracted implicit information is highly dependent on the specific CDR model, and is therefore not easily reusable or transferable. Additionally, the extracted implicit information only appears within the intermediate substructure of specific CDRs during training and is thus not easily retained for more use. In light of these challenges, this paper proposes AutoTransfer, with an Instance Transfer Policy Network, to selectively transfers instances from source domain to target domain for improved recommendations. Specifically, AutoTransfer acts as an agent that adaptively selects a subset of informative and transferable instances from the source domain. Notably, the selected subset possesses extraordinary re-utilization property that can be saved for improving model training of various future RS models in target domain. Experimental results on two public CDR benchmark datasets demonstrate that the proposed method outperforms state-of-the-art CDR baselines and classic Single-Domain Recommendation (SDR) approaches. The implementation code is available for easy reproduction.","PeriodicalId":425056,"journal":{"name":"Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval","volume":"75 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":"127675150","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

Computational Versus Perceived Popularity Miscalibration in Recommender Systems 推荐系统中的计算误差与感知误差

Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval Pub Date : 2023-07-18 DOI: 10.1145/3539618.3591964

Oleg Lesota, Gustavo Escobedo, Yashar Deldjoo, B. Ferwerda, Simone Kopeinik, E. Lex, Navid Rekabsaz, M. Schedl

{"title":"Computational Versus Perceived Popularity Miscalibration in Recommender Systems","authors":"Oleg Lesota, Gustavo Escobedo, Yashar Deldjoo, B. Ferwerda, Simone Kopeinik, E. Lex, Navid Rekabsaz, M. Schedl","doi":"10.1145/3539618.3591964","DOIUrl":"https://doi.org/10.1145/3539618.3591964","url":null,"abstract":"Popularity bias in recommendation lists refers to over-representation of popular content and is a challenge for many recommendation algorithms. Previous research has suggested several offline metrics to quantify popularity bias, which commonly relate the popularity of items in users' recommendation lists to the popularity of items in their interaction history. Discrepancies between these two factors are referred to as popularity miscalibration. While popularity metrics provide a straightforward and well-defined means to measure popularity bias, it is unknown whether they actually reflect users' perception of popularity bias. To address this research gap, we conduct a crowd-sourced user study on Prolific, involving 56 participants, to (1) investigate whether the level of perceived popularity miscalibration differs between common recommendation algorithms, (2) assess the correlation between perceived popularity miscalibration and its corresponding quantification according to a common offline metric. We conduct our study in a well-defined and important domain, namely music recommendation using the standardized LFM-2b dataset, and quantify popularity miscalibration of five recommendation algorithms by utilizing Jensen-Shannon distance (JSD). Challenging the findings of previous studies, we observe that users generally do perceive significant differences in terms of popularity bias between algorithms if this bias is framed as popularity miscalibration. In addition, JSD correlates moderately with users' perception of popularity, but not with their perception of unpopularity.","PeriodicalId":425056,"journal":{"name":"Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval","volume":"48 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":"127887462","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

StreamE: Learning to Update Representations for Temporal Knowledge Graphs in Streaming Scenarios 流:学习在流场景中更新时态知识图的表示

Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval Pub Date : 2023-07-18 DOI: 10.1145/3539618.3591772

Jiasheng Zhang, Jie Shao, Bin Cui

{"title":"StreamE: Learning to Update Representations for Temporal Knowledge Graphs in Streaming Scenarios","authors":"Jiasheng Zhang, Jie Shao, Bin Cui","doi":"10.1145/3539618.3591772","DOIUrl":"https://doi.org/10.1145/3539618.3591772","url":null,"abstract":"Learning representations for temporal knowledge graphs (TKGs) is a fundamental task. Most existing methods regard TKG as a sequence of static snapshots and recurrently learn representations by retracing the previous snapshots. However, new knowledge can be continuously accrued to TKGs as streams. These methods either cannot handle new entities or fail to update representations in real time, making them unfeasible to adapt to the streaming scenarios. In this paper, we propose a lightweight framework called StreamE towards the efficient generation of TKG representations in streaming scenarios. To reduce the parameter size, entity representations in StreamE are decoupled from the model training to serve as the memory module to store the historical information of entities. To achieve efficient update and generation, the process of generating representations is decoupled as two functions in StreamE. An update function is learned to incrementally update entity representations based on the newly-arrived knowledge and a read function is learned to predict the future semantics of entity representations. The update function avoids the recurrent modeling paradigm and thus gains high efficiency while the read function considers multiple semantic change properties. We further propose a joint training strategy with two temporal regularizations to effectively optimize the framework. Experimental results show that StreamE can achieve better performance than baseline methods with 100x faster in inference, 25x faster in training, and only 1/5 parameter size, which demonstrates its superiority. Code is available at https://github.com/zjs123/StreamE.","PeriodicalId":425056,"journal":{"name":"Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval","volume":"1 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":"129006197","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

The JOKER Corpus: English-French Parallel Data for Multilingual Wordplay Recognition JOKER语料库:用于多语言文字游戏识别的英法平行数据

Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval Pub Date : 2023-07-18 DOI: 10.1145/3539618.3591885

Liana Ermakova, Anne-Gwenn Bosser, A. Jatowt, Tristan Miller

{"title":"The JOKER Corpus: English-French Parallel Data for Multilingual Wordplay Recognition","authors":"Liana Ermakova, Anne-Gwenn Bosser, A. Jatowt, Tristan Miller","doi":"10.1145/3539618.3591885","DOIUrl":"https://doi.org/10.1145/3539618.3591885","url":null,"abstract":"Despite recent advances in information retrieval and natural language processing, rhetorical devices that exploit ambiguity or subvert linguistic rules remain a challenge for such systems. However, corpus-based analysis of wordplay has been a perennial topic of scholarship in the humanities, including literary criticism, language education, and translation studies. The immense data-gathering effort required for these studies points to the need for specialized text retrieval and classification technology, and consequently for appropriate test collections. In this paper, we introduce and analyze a new dataset for research and applications in the retrieval and processing of wordplay. Developed for the JOKER track at CLEF 2023, our annotated corpus extends and improves upon past English wordplay detection datasets in several ways. First, we introduce hundreds of additional positive examples of wordplay; second, we provide French translations for the examples; and third, we provide negative examples of non-wordplay with characteristics closely matching those of the positive examples. This last feature helps ensure that AI models learn to effectively distinguish wordplay from non-wordplay, and not simply texts differing in length, style, or vocabulary. Our test collection represents then a step towards wordplay-aware multilingual information retrieval.","PeriodicalId":425056,"journal":{"name":"Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval","volume":"1 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":"128718401","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

Fine-Grained Preference-Aware Personalized Federated POI Recommendation with Data Sparsity 具有数据稀疏性的细粒度偏好感知个性化联邦POI推荐

Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval Pub Date : 2023-07-18 DOI: 10.1145/3539618.3591688

Xiao Zhang, Ziming Ye, Jianfeng Lu, Fuzhen Zhuang, Yanwei Zheng, Dongxiao Yu

{"title":"Fine-Grained Preference-Aware Personalized Federated POI Recommendation with Data Sparsity","authors":"Xiao Zhang, Ziming Ye, Jianfeng Lu, Fuzhen Zhuang, Yanwei Zheng, Dongxiao Yu","doi":"10.1145/3539618.3591688","DOIUrl":"https://doi.org/10.1145/3539618.3591688","url":null,"abstract":"With the raised privacy concerns and rigorous data regulations, federated learning has become a hot collaborative learning paradigm for the recommendation model without sharing the highly sensitive POI data. However, the time-sensitive, heterogeneous, and limited POI records seriously restrict the development of federated POI recommendation. To this end, in this paper, we design the fine-grained preference-aware personalized federated POI recommendation framework, namely PrefFedPOI, under extremely sparse historical trajectories to address the above challenges. In details, PrefFedPOI extracts the fine-grained preference of current time slot by combining historical recent preferences and periodic preferences within each local client. Due to the extreme lack of POI data in some time slots, a data amount aware selective strategy is designed for model parameters uploading. Moreover, a performance enhanced clustering mechanism with reinforcement learning is proposed to capture the preference relatedness among all clients to encourage the positive knowledge sharing. Furthermore, a clustering teacher network is designed for improving efficiency by clustering guidance. Extensive experiments are conducted on two diverse real-world datasets to demonstrate the effectiveness of proposed PrefFedPOI comparing with state-of-the-arts. In particular, personalized PrefFedPOI can achieve 7% accuracy improvement on average among data-sparsity clients.","PeriodicalId":425056,"journal":{"name":"Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval","volume":"38 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":"115935139","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