Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval最新文献_第2页

Personalized Dynamic Recommender System for Investors 投资者个性化动态推荐系统

Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval Pub Date : 2023-07-18 DOI: 10.1145/3539618.3592035

Takehiro Takayanagi, Chung-Chi Chen, K. Izumi

{"title":"Personalized Dynamic Recommender System for Investors","authors":"Takehiro Takayanagi, Chung-Chi Chen, K. Izumi","doi":"10.1145/3539618.3592035","DOIUrl":"https://doi.org/10.1145/3539618.3592035","url":null,"abstract":"With the development of online platforms, people can share and obtain opinions quickly. It also makes individuals' preferences change dynamically and rapidly because they may change their minds when getting convincing opinions from other users. Unlike representative areas of recommendation research such as e-commerce platforms where items' features are fixed, in investment scenarios financial instruments' features such as stock price, also change dynamically over time. To capture these dynamic features and provide a better-personalized recommendation for amateur investors, this study proposes a Personalized Dynamic Recommender System for Investors, PDRSI. The proposed PDRSI considers two investor's personal features: dynamic preferences and historical interests, and two temporal environmental properties: recent discussions on the social media platform and the latest market information. The experimental results support the usefulness of the proposed PDRSI, and the ablation studies show the effect of each module. For reproduction, we follow Twitter's developer policy to share our dataset for future work.","PeriodicalId":425056,"journal":{"name":"Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval","volume":"478 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":"129765004","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

A Practical Online Allocation Framework at Industry-scale in Constrained Recommendation 约束推荐下工业规模在线配置的实用框架

Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval Pub Date : 2023-07-18 DOI: 10.1145/3539618.3591835

Daohong Jian, Yang Bao, Jun Zhou, Hua Wu

引用次数: 0

Rows or Columns? Minimizing Presentation Bias When Comparing Multiple Recommender Systems 行还是列?在比较多个推荐系统时最小化呈现偏差

Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval Pub Date : 2023-07-18 DOI: 10.1145/3539618.3592056

Patrik Dokoupil, Ladislav Peška, Ludovico Boratto

{"title":"Rows or Columns? Minimizing Presentation Bias When Comparing Multiple Recommender Systems","authors":"Patrik Dokoupil, Ladislav Peška, Ludovico Boratto","doi":"10.1145/3539618.3592056","DOIUrl":"https://doi.org/10.1145/3539618.3592056","url":null,"abstract":"Going beyond accuracy in the evaluation of a recommender system is an aspect that is receiving more and more attention. Among the many perspectives that can be considered, the impact of presentation bias is of central importance. Under presentation bias, the attention of the users to the items in a recommendation list changes, thus affecting their possibility to be considered and the effectiveness of a model. Page-wise within-subject studies are widely employed in the recommender systems literature to compare algorithms by displaying their results in parallel. However, no study has ever been performed to assess the impact of presentation bias in this context. In this paper, we characterize how presentation bias affects different layout options, which present the results in column- or row-wise fashion. Concretely, we present a user study where six layout variants are proposed to the users in a page-wise within-subject setting, so as to evaluate their perception of the displayed recommendations. Results show that presentation bias impacts users clicking behavior (low-level feedback), but not so much the perceived performance of a recommender system (high-level feedback). Source codes and raw results are available at https://tinyurl.com/PresBiasSIGIR2023.","PeriodicalId":425056,"journal":{"name":"Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval","volume":"188 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":"129211207","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

ADL: Adaptive Distribution Learning Framework for Multi-Scenario CTR Prediction ADL:多场景CTR预测的自适应分布学习框架

Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval Pub Date : 2023-07-18 DOI: 10.1145/3539618.3591944

Jinyun Li, Huiwen Zheng, Yuan-Cheng Liu, Minfang Lu, Lixia Wu, Haoyuan Hu

{"title":"ADL: Adaptive Distribution Learning Framework for Multi-Scenario CTR Prediction","authors":"Jinyun Li, Huiwen Zheng, Yuan-Cheng Liu, Minfang Lu, Lixia Wu, Haoyuan Hu","doi":"10.1145/3539618.3591944","DOIUrl":"https://doi.org/10.1145/3539618.3591944","url":null,"abstract":"Large-scale commercial platforms usually involve numerous business scenarios for diverse business strategies. To provide click-through rate (CTR) predictions for multiple scenarios simultaneously, existing promising multi-scenario models explicitly construct scenario-specific networks by manually grouping scenarios based on particular business strategies. Nonetheless, this pre-defined data partitioning process heavily relies on prior knowledge, and it may neglect the underlying data distribution of each scenario, hence limiting the model's representation capability. Regarding the above issues, we propose Adaptive Distribution Learning (ADL): an end-to-end optimization distribution framework which is composed of a clustering process and classification process. Specifically, we design a distribution adaptation module with a customized dynamic routing mechanism. Instead of introducing prior knowledge for pre-defined data allocation, this routing algorithm adaptively provides a distribution coefficient for each sample to determine which cluster it belongs to. Each cluster corresponds to a particular distribution so that the model can sufficiently capture the commonalities and distinctions between these distinct clusters. Our results on both public and large-scale industrial datasets show the effectiveness and efficiency of ADL: the model yields impressive prediction accuracy with more than 50% reduction in time cost during the training phase when compared to other methods.","PeriodicalId":425056,"journal":{"name":"Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval","volume":"22 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":"129327547","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

Gated Attention with Asymmetric Regularization for Transformer-based Continual Graph Learning 基于变压器的连续图学习的非对称正则化门控注意

Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval Pub Date : 2023-07-18 DOI: 10.1145/3539618.3591991

Hongxiang Lin, Ruiqi Jia, Xiaoqing Lyu

{"title":"Gated Attention with Asymmetric Regularization for Transformer-based Continual Graph Learning","authors":"Hongxiang Lin, Ruiqi Jia, Xiaoqing Lyu","doi":"10.1145/3539618.3591991","DOIUrl":"https://doi.org/10.1145/3539618.3591991","url":null,"abstract":"Continual graph learning (CGL) aims to mitigate the topological-feature-induced catastrophic forgetting problem (TCF) in graph neural networks, which plays an essential role in the field of information retrieval. The TCF is mainly caused by the forgetting of node features of old tasks and the forgetting of topological features shared by old and new tasks. Existing CGL methods do not pay enough attention to the forgetting of topological features shared between different tasks. In this paper, we propose a transformer-based CGL method (Trans-CGL), thereby taking full advantage of the transformer's properties to mitigate the TCF problem. Specifically, to alleviate forgetting of node features, we introduce a gated attention mechanism for Trans-CGL based on parameter isolation that allows the model to be independent of each other when learning old and new tasks. Furthermore, to address the forgetting of shared parameters that store topological information between different tasks, we propose an asymmetric mask attention regularization module to constrain the shared attention parameters ensuring that the shared topological information is preserved. Comparative experiments show that the method achieves competitive performance on four real-world datasets.","PeriodicalId":425056,"journal":{"name":"Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval","volume":"16 4 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":"124617491","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

Contrastive Box Embedding for Collaborative Reasoning 协同推理的对比盒嵌入

Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval Pub Date : 2023-07-18 DOI: 10.1145/3539618.3591654

Tingting Liang, Yuanqing Zhang, Qianhui Di, Congying Xia, Youhuizi Li, Yuyu Yin

{"title":"Contrastive Box Embedding for Collaborative Reasoning","authors":"Tingting Liang, Yuanqing Zhang, Qianhui Di, Congying Xia, Youhuizi Li, Yuyu Yin","doi":"10.1145/3539618.3591654","DOIUrl":"https://doi.org/10.1145/3539618.3591654","url":null,"abstract":"Most of the existing personalized recommendation methods predict the probability that one user might interact with the next item by matching their representations in the latent space. However, as a cognitive task, it is essential for an impressive recommender system to acquire the cognitive capacity rather than to decide the users' next steps by learning the pattern from the historical interactions through matching-based objectives. Therefore, in this paper, we propose to model the recommendation as a logical reasoning task which is more in line with an intelligent recommender system. Different from the prior works, we embed each query as a box rather than a single point in the vector space, which is able to model sets of users or items enclosed and logical operators (e.g., intersection) over boxes in a more natural manner. Although modeling the logical query with box embedding significantly improves the previous work of reasoning-based recommendation, there still exist two intractable issues including aggregation of box embeddings and training stalemate in critical point of boxes. To tackle these two limitations, we propose a Contrastive Box learning framework for Collaborative Reasoning (CBox4CR). Specifically, CBox4CR combines a smoothed box volume-based contrastive learning objective with the logical reasoning objective to learn the distinctive box representations for the user's preference and the logical query based on the historical interaction sequence. Extensive experiments conducted on four publicly available datasets demonstrate the superiority of our CBox4CR over the state-of-the-art models in recommendation task.","PeriodicalId":425056,"journal":{"name":"Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval","volume":"47 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":"130381176","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

Long-Form Information Retrieval for Enterprise Matchmaking 面向企业配对的长格式信息检索

Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval Pub Date : 2023-07-18 DOI: 10.1145/3539618.3591833

Pengyuan Li, G. Ren, Anna Lisa Gentile, Chad DeLuca, Daniel Tan, Sandeep Gopisetty

引用次数: 0

Model-free Reinforcement Learning with Stochastic Reward Stabilization for Recommender Systems 基于随机奖励稳定的无模型强化学习推荐系统

Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval Pub Date : 2023-07-18 DOI: 10.1145/3539618.3592022

Tianchi Cai, Shenliao Bao, Jiyan Jiang, Shiji Zhou, Wenpeng Zhang, Lihong Gu, Jinjie Gu, Guannan Zhang

{"title":"Model-free Reinforcement Learning with Stochastic Reward Stabilization for Recommender Systems","authors":"Tianchi Cai, Shenliao Bao, Jiyan Jiang, Shiji Zhou, Wenpeng Zhang, Lihong Gu, Jinjie Gu, Guannan Zhang","doi":"10.1145/3539618.3592022","DOIUrl":"https://doi.org/10.1145/3539618.3592022","url":null,"abstract":"Model-free RL-based recommender systems have recently received increasing research attention due to their capability to handle partial feedback and long-term rewards. However, most existing research has ignored a critical feature in recommender systems: one user's feedback on the same item at different times is random. The stochastic rewards property essentially differs from that in classic RL scenarios with deterministic rewards, which makes RL-based recommender systems much more challenging. In this paper, we first demonstrate in a simulator environment where using direct stochastic feedback results in a significant drop in performance. Then to handle the stochastic feedback more efficiently, we design two stochastic reward stabilization frameworks that replace the direct stochastic feedback with that learned by a supervised model. Both frameworks are model-agnostic, i.e., they can effectively utilize various supervised models. We demonstrate the superiority of the proposed frameworks over different RL-based recommendation baselines with extensive experiments on a recommendation simulator as well as an industrial-level recommender system.","PeriodicalId":425056,"journal":{"name":"Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval","volume":"71 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":"130651660","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

PLATE: A Prompt-Enhanced Paradigm for Multi-Scenario Recommendations PLATE:多场景推荐的快速增强范例

Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval Pub Date : 2023-07-18 DOI: 10.1145/3539618.3591750

Yuhao Wang, Xiangyu Zhao, Bo Chen, Qidong Liu, Huifeng Guo, Huanshuo Liu, Yichao Wang, Rui Zhang, Ruiming Tang

{"title":"PLATE: A Prompt-Enhanced Paradigm for Multi-Scenario Recommendations","authors":"Yuhao Wang, Xiangyu Zhao, Bo Chen, Qidong Liu, Huifeng Guo, Huanshuo Liu, Yichao Wang, Rui Zhang, Ruiming Tang","doi":"10.1145/3539618.3591750","DOIUrl":"https://doi.org/10.1145/3539618.3591750","url":null,"abstract":"With the explosive growth of commercial applications of recommender systems, multi-scenario recommendation (MSR) has attracted considerable attention, which utilizes data from multiple domains to improve their recommendation performance simultaneously. However, training a unified deep recommender system (DRS) may not explicitly comprehend the commonality and difference among domains, whereas training an individual model for each domain neglects the global information and incurs high computation costs. Likewise, fine-tuning on each domain is inefficient, and recent advances that apply the prompt tuning technique to improve fine-tuning efficiency rely solely on large-sized transformers. In this work, we propose a novel prompt-enhanced paradigm for multi-scenario recommendation. Specifically, a unified DRS backbone model is first pre-trained using data from all the domains in order to capture the commonality across domains. Then, we conduct prompt tuning with two novel prompt modules, capturing the distinctions among various domains and users. Our experiments on Douban, Amazon, and Ali-CCP datasets demonstrate the effectiveness of the proposed paradigm with two noticeable strengths: (i) its great compatibility with various DRS backbone models, and (ii) its high computation and storage efficiency with only 6% trainable parameters in prompt tuning phase. 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":"48 41 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":"124221627","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

Decoupled Hyperbolic Graph Attention Network for Cross-domain Named Entity Recognition 跨域命名实体识别解耦双曲图注意网络

Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval Pub Date : 2023-07-18 DOI: 10.1145/3539618.3591662

Jingyun Xu, Yi Cai

{"title":"Decoupled Hyperbolic Graph Attention Network for Cross-domain Named Entity Recognition","authors":"Jingyun Xu, Yi Cai","doi":"10.1145/3539618.3591662","DOIUrl":"https://doi.org/10.1145/3539618.3591662","url":null,"abstract":"To address the scarcity of massive labeled data, cross-domain named entity recognition (cross-domain NER) attracts increasing attention. Recent studies focus on decomposing NER into two separate tasks (i.e., entity span detection and entity type classification) to reduce the complexity of the cross-domain transfer. Despite the promising results, there still exists room for improvement. In particular, the rich domain-shared syntactic and semantic information, which are respectively important for entity span detection and entity type classification, are still underutilized. In light of these two challenges, we propose applying graph attention networks (GATs) to encode the above two kinds of information. Moreover, considering that GATs mainly operate in the Euclidean space, which may fail to capture the latent hierarchical relations among words for learning high-quality word representations, we further propose to embed words into Hyperbolic spaces. Finally, a decouple hyperbolic graph attention network (DH-GAT) is introduced for cross-domain NER. Empirical results on 10 domain pairs show that DH-GAT achieves state-of-the-art performance on several standard metrics, and further analyses are presented to better understand each component's effectiveness.","PeriodicalId":425056,"journal":{"name":"Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval","volume":"29 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":"123469653","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