Proceedings of the 31st ACM International Conference on Information & Knowledge Management最新文献_第3页

CFS-MTL: A Causal Feature Selection Mechanism for Multi-task Learning via Pseudo-intervention 伪干预多任务学习的CFS-MTL因果特征选择机制

Proceedings of the 31st ACM International Conference on Information & Knowledge Management Pub Date : 2022-10-17 DOI: 10.1145/3511808.3557559

Zhongde Chen, Ruize Wu, Cong Jiang, Honghui Li, Xin Dong, Can Long, Yong He, Lei Cheng, Linjian Mo

{"title":"CFS-MTL: A Causal Feature Selection Mechanism for Multi-task Learning via Pseudo-intervention","authors":"Zhongde Chen, Ruize Wu, Cong Jiang, Honghui Li, Xin Dong, Can Long, Yong He, Lei Cheng, Linjian Mo","doi":"10.1145/3511808.3557559","DOIUrl":"https://doi.org/10.1145/3511808.3557559","url":null,"abstract":"Multi-task learning (MTL) has been successfully applied to a wide range of real-world applications. However, MTL models often suffer from performance degradation with negative transfer due to sharing all features without distinguishing their helpfulness for all tasks. To this end, many works on feature selection for multi-task learning (FS-MTL) have been proposed to alleviate negative transfer between tasks by learning features selectively for each specific task. However, due to latent confounders between features and task targets, the correlations captured by the feature selection modules proposed in these works may fail to reflect the actual effect of the features on the targets. This paper explains negative transfer in FS-MTL from a causal perspective and presents a novel architecture called Causal Feature Selection for Multi-task Learning(CFS-MTL). This method incorporates the idea of causal inference into feature selection for multi-task learning via pseudo-intervention. It aims to select features with more stable causal effects rather than spurious correlations for each task by regularizing the distance between feature ITEs and feature importance. We conduct extensive experiments based on three real-world datasets to demonstrate that our proposed CFS-MTL outperforms state-of-the-art MTL models significantly in the AUC metric.","PeriodicalId":389624,"journal":{"name":"Proceedings of the 31st ACM International Conference on Information & Knowledge Management","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129353613","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

Multi-scale Multi-modal Dictionary BERT For Effective Text-image Retrieval in Multimedia Advertising 基于多尺度多模态BERT的多媒体广告文本图像检索

Proceedings of the 31st ACM International Conference on Information & Knowledge Management Pub Date : 2022-10-17 DOI: 10.1145/3511808.3557653

Tan Yu, Jie Liu, Zhipeng Jin, Yi Yang, Hongliang Fei, Ping Li

{"title":"Multi-scale Multi-modal Dictionary BERT For Effective Text-image Retrieval in Multimedia Advertising","authors":"Tan Yu, Jie Liu, Zhipeng Jin, Yi Yang, Hongliang Fei, Ping Li","doi":"10.1145/3511808.3557653","DOIUrl":"https://doi.org/10.1145/3511808.3557653","url":null,"abstract":"Visual content in multimedia advertising effectively attracts the customer's attention. Search-based multimedia advertising is a cross-modal retrieval problem. Due to the modal gap between texts and images/videos, cross-modal image/video retrieval is a challenging problem. Recently, multi-modal dictionary BERT has bridged the model gap by unifying the images/videos and texts from different modalities through a multi-modal dictionary. In this work, we improve the multi-modal dictionary BERT by developing a multi-scale multi-modal dictionary and propose a Multi-scale Multi-modal Dictionary BERT (M^2D-BERT). The multi-scale dictionary partitions the feature space into different levels and is effective in describing the fine-level relevance and the coarse-level relevance between the text and images. Meanwhile, we constrain that the code-words in dictionaries from different scales to be orthogonal to each other. Thus, it ensures multiple dictionaries are complementary to each other. Moreover, we adopt a two-level residual quantization to enhance the capacity of each multi-modal dictionary. Systematic experiments conducted on large-scale cross-modal retrieval datasets demonstrate the excellent performance of our M2D-BERT.","PeriodicalId":389624,"journal":{"name":"Proceedings of the 31st ACM International Conference on Information & Knowledge Management","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130510989","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

Deep Ordinal Neural Network for Length of Stay Estimation in the Intensive Care Units 重症监护病房住院时间估计的深度有序神经网络

Proceedings of the 31st ACM International Conference on Information & Knowledge Management Pub Date : 2022-10-17 DOI: 10.1145/3511808.3557578

D. Cai, Moxian Song, Chenxi Sun, B. Zhang, linda Qiao, Hongyan Li

{"title":"Deep Ordinal Neural Network for Length of Stay Estimation in the Intensive Care Units","authors":"D. Cai, Moxian Song, Chenxi Sun, B. Zhang, linda Qiao, Hongyan Li","doi":"10.1145/3511808.3557578","DOIUrl":"https://doi.org/10.1145/3511808.3557578","url":null,"abstract":"Length of Stay (LoS) estimation is important for efficient healthcare resource management. Since the distribution of LoS is highly skewed, some previous works frame the LoS estimation as a multi-class classification problem by dividing the range of LoS into buckets. However, they ignore the ordinal relationship between labels. The distribution of bucketed LoS, with a heavy head and a heavy tail, is still imbalanced since the long tail is grouped into the last bucket. This paper proposes a Deep Ordinal neural network for Length of stay Estimation in the intensive care units (DOSE). DOSE can exploit the ordinal relationship and mitigate the skewness. The ordinal classification problem is decomposed into a series of binary classification sub-problems by using multiple binary classifiers. To maintain consistency among binary classifiers, the monotonicity constraint penalty is proposed. The number of samples whose labels are higher or lower than a given threshold is at the same level due to the heavy head and tail of the distribution. Therefore, the training data of each binary classifier are balanced. Experiments are conducted on the real-world healthcare dataset. DOSE outperforms all baseline methods in all metrics. The distribution of the prediction of DOSE is more aligned with the ground truth.","PeriodicalId":389624,"journal":{"name":"Proceedings of the 31st ACM International Conference on Information & Knowledge Management","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130527766","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

MAE4Rec: Storage-saving Transformer for Sequential Recommendations MAE4Rec:存储节省变压器顺序建议

Proceedings of the 31st ACM International Conference on Information & Knowledge Management Pub Date : 2022-10-17 DOI: 10.1145/3511808.3557461

Kesen Zhao, Xiangyu Zhao, Zijian Zhang, Muyang Li

{"title":"MAE4Rec: Storage-saving Transformer for Sequential Recommendations","authors":"Kesen Zhao, Xiangyu Zhao, Zijian Zhang, Muyang Li","doi":"10.1145/3511808.3557461","DOIUrl":"https://doi.org/10.1145/3511808.3557461","url":null,"abstract":"Sequential recommender systems (SRS) aim to infer the users' preferences from their interaction history and predict items that will be of interest to the users. The majority of SRS models typically incorporate all historical interactions for next-item recommendations. Despite their success, feeding all interactions into the model without filtering may lead to severe practical issues: (i) redundant interactions hinder the SRS model from capturing the users' intentions; (ii) the computational cost is huge, as the computational complexity is proportional to the length of the interaction sequence; (iii) more memory space is necessitated to store all interaction records from all users. To this end, we propose a novel storage-saving SRS framework, MAE4Rec, based on a unidirectional self-attentive mechanism and masked autoencoder. Specifically, in order to lower the storage consumption, MAE4Rec first masks and discards a large percentage of historical interactions, and then infers the next interacted item solely based on the latent representation of unmarked ones. Experiments on two real-world datasets demonstrate that the proposed model achieves competitive performance against state-of-the-art SRS models with more than 40% compression of storage.","PeriodicalId":389624,"journal":{"name":"Proceedings of the 31st ACM International Conference on Information & Knowledge Management","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123958618","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

A Practical Distributed ADMM Solver for Billion-Scale Generalized Assignment Problems 十亿尺度广义分配问题的实用分布式ADMM求解器

Proceedings of the 31st ACM International Conference on Information & Knowledge Management Pub Date : 2022-10-17 DOI: 10.1145/3511808.3557148

Jun Zhou, Feng Qi, Zhigang Hua, Daohong Jian, Ziqi Liu, Hua Wu

{"title":"A Practical Distributed ADMM Solver for Billion-Scale Generalized Assignment Problems","authors":"Jun Zhou, Feng Qi, Zhigang Hua, Daohong Jian, Ziqi Liu, Hua Wu","doi":"10.1145/3511808.3557148","DOIUrl":"https://doi.org/10.1145/3511808.3557148","url":null,"abstract":"Assigning items to owners is a common problem found in various real-world applications, for example, audience-channel matching in marketing campaigns, borrower-lender matching in loan management, and shopper-merchant matching in e-commerce. Given an objective and multiple constraints, an assignment problem can be formulated as a constrained optimization problem. Such assignment problems are usually NP-hard [21], so when the number of items or the number of owners is large, solving for exact solutions becomes challenging. In this paper, we are interested in solving constrained assignment problems with hundreds of millions of items. Thus, with just tens of owners, the number of decision variables is at billion-scale. This scale is usually seen in the internet industry, which makes decisions for large groups of users. We relax the possible integer constraint, and formulate a general optimization problem that covers commonly seen assignment problems. Its objective function is convex. Its constraints are either linear, or convex and separable by items. We study to solve our generalized assignment problems in the Bregman Alternating Direction Method of Multipliers (BADMM) framework where we exploit Bregman divergence to transform the Augmented Lagrangian into a separable form, and solve many subproblems in parallel. The entire solution can thus be implemented using a MapReduce-style distributed computation framework. We present experiment results on both synthetic and real-world datasets to verify its accuracy and scalability.","PeriodicalId":389624,"journal":{"name":"Proceedings of the 31st ACM International Conference on Information & Knowledge Management","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123333166","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

DuMapper: Towards Automatic Verification of Large-Scale POIs with Street Views at Baidu Maps DuMapper:基于百度地图街景的大规模poi自动验证

Proceedings of the 31st ACM International Conference on Information & Knowledge Management Pub Date : 2022-10-17 DOI: 10.1145/3511808.3557097

Miao Fan, Jizhou Huang, Haifeng Wang

{"title":"DuMapper: Towards Automatic Verification of Large-Scale POIs with Street Views at Baidu Maps","authors":"Miao Fan, Jizhou Huang, Haifeng Wang","doi":"10.1145/3511808.3557097","DOIUrl":"https://doi.org/10.1145/3511808.3557097","url":null,"abstract":"With the increased popularity of mobile devices, Web mapping services have become an indispensable tool in our daily lives. To provide user-satisfied services, such as location searches, the point of interest (POI) database is the fundamental infrastructure, as it archives multimodal information on billions of geographic locations closely related to people's lives, such as a shop or a bank. Therefore, verifying the correctness of a large-scale POI database is vital. To achieve this goal, many industrial companies adopt volunteered geographic information (VGI) platforms that enable thousands of crowdworkers and expert mappers to verify POIs seamlessly; but to do so, they have to spend millions of dollars every year. To save the tremendous labor costs, we devised DuMapper, an automatic system for large-scale POI verification with the multimodal street-view data at Baidu Maps. This paper presents not only DuMapper I, which imitates the process of POI verification conducted by expert mappers, but also proposes DuMapper II, a highly efficient framework to accelerate POI verification by means of deep multimodal embedding and approximate nearest neighbor (ANN) search. DuMapper II takes the signboard image and the coordinates of a real-world place as input to generate a low-dimensional vector, which can be leveraged by ANN algorithms to conduct a more accurate search through billions of archived POIs in the database for verification within milliseconds. Compared with DuMapper I, experimental results demonstrate that DuMapper II can significantly increase the throughput of POI verification by 50 times. DuMapper has already been deployed in production since June 2018, which dramatically improves the productivity and efficiency of POI verification at Baidu Maps. As of December 31, 2021, it has enacted over 405 million iterations of POI verification within a 3.5-year period, representing an approximate workload of 800 high-performance expert mappers.","PeriodicalId":389624,"journal":{"name":"Proceedings of the 31st ACM International Conference on Information & Knowledge Management","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123342436","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: 4

MDGCF: Multi-Dependency Graph Collaborative Filtering with Neighborhood- and Homogeneous-level Dependencies MDGCF:具有邻域和同质级依赖关系的多依赖图协同过滤

Proceedings of the 31st ACM International Conference on Information & Knowledge Management Pub Date : 2022-10-17 DOI: 10.1145/3511808.3557390

Guohui Li, Zhiqiang Guo, Jianjun Li, Chaoyang Wang

{"title":"MDGCF: Multi-Dependency Graph Collaborative Filtering with Neighborhood- and Homogeneous-level Dependencies","authors":"Guohui Li, Zhiqiang Guo, Jianjun Li, Chaoyang Wang","doi":"10.1145/3511808.3557390","DOIUrl":"https://doi.org/10.1145/3511808.3557390","url":null,"abstract":"Due to the success of graph convolutional networks (GCNs) in effectively extracting features in non-Euclidean spaces, GCNs has become the rising star in implicit collaborative filtering. Existing works, while encouraging, typically adopt simple aggregation operation on the user-item bipartite graph to model user and item representations, but neglect to mine the sufficient dependencies between nodes, e.g., the relationships between users/items and their neighbors (or congeners), resulting in inadequate graph representation learning. To address these problems, we propose a novel Multi-Dependency Graph Collaborative Filtering (MDGCF) model, which mines the neighborhood- and homogeneous-level dependencies to enhance the representation power of graph-based CF models. Specifically, for neighborhood-level dependencies, we explicitly consider both popularity score and preference correlation by designing a joint neighborhood-level dependency weight, based on which we construct a neighborhood-level dependencies graph to capture higher-order interaction features. Besides, by adaptively mining the homogeneous-level dependencies among users and items, we construct two homogeneous graphs, based on which we further aggregate features from homogeneous users and items to supplement their representations, respectively. Extensive experiments on three real-world benchmark datasets demonstrate the effectiveness of the proposed MDGCF. Further experiments reveal that our model can capture rich dependencies between nodes for explaining user behaviors.","PeriodicalId":389624,"journal":{"name":"Proceedings of the 31st ACM International Conference on Information & Knowledge Management","volume":"15 5","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121175776","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

High Availability Framework and Query Fault Tolerance for Hybrid Distributed Database Systems 混合分布式数据库系统的高可用性框架与查询容错

Proceedings of the 31st ACM International Conference on Information & Knowledge Management Pub Date : 2022-10-17 DOI: 10.1145/3511808.3557086

Krishna Kantikiran Pasupuleti, B. Klots, Vijayakrishnan Nagarajan, Ananthakiran Kandukuri, N. Agarwal

{"title":"High Availability Framework and Query Fault Tolerance for Hybrid Distributed Database Systems","authors":"Krishna Kantikiran Pasupuleti, B. Klots, Vijayakrishnan Nagarajan, Ananthakiran Kandukuri, N. Agarwal","doi":"10.1145/3511808.3557086","DOIUrl":"https://doi.org/10.1145/3511808.3557086","url":null,"abstract":"Modern commercial database systems are increasingly evolving into a hybrid distributed system model where a primary database host system enlists the services of a loosely coupled secondary system that acts as an accelerator. Often the secondary system is a distributed system that can perform specific tasks massively parallelized with results fed back to the host database. Similar models can also be seen in architectures that separate compute from storage. As the scale of the system grows, failures of nodes become common, and the architectural goal is to recover the system with minimal disruption to the workload as seen by the user. This paper introduces a new framework that allows a host database to efficiently manage the availability of a massive secondary distributed system and describes a mechanism to achieve query fault tolerance at the primary database by transparently re-executing query (sub)plans on the secondary distributed system. The focus is on improving two important aspects of disruption ? downtime and transparency to the user. The proposed mechanisms achieve quick recovery, reduced duration of downtime and isolation of errors during query execution, thus improving execution transparency for the users.","PeriodicalId":389624,"journal":{"name":"Proceedings of the 31st ACM International Conference on Information & Knowledge Management","volume":"150 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121399262","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

Is It Enough Just Looking at the Title?: Leveraging Body Text To Enrich Title Words Towards Accurate News Recommendation 只看标题就够了吗?:利用正文丰富标题词，实现准确的新闻推荐

Proceedings of the 31st ACM International Conference on Information & Knowledge Management Pub Date : 2022-10-17 DOI: 10.1145/3511808.3557619

Taeho Kim, Yungi Kim, Yeon-Chang Lee, Won-Yong Shin, Sang-Wook Kim

引用次数: 5

Multimodal Meta-Learning for Cold-Start Sequential Recommendation 冷启动顺序推荐的多模态元学习

Proceedings of the 31st ACM International Conference on Information & Knowledge Management Pub Date : 2022-10-17 DOI: 10.1145/3511808.3557101

Xingyu Pan, Yushuo Chen, Changxin Tian, Zihan Lin, Jinpeng Wang, He Hu, Wayne Xin Zhao

{"title":"Multimodal Meta-Learning for Cold-Start Sequential Recommendation","authors":"Xingyu Pan, Yushuo Chen, Changxin Tian, Zihan Lin, Jinpeng Wang, He Hu, Wayne Xin Zhao","doi":"10.1145/3511808.3557101","DOIUrl":"https://doi.org/10.1145/3511808.3557101","url":null,"abstract":"In this paper, we study the task of cold-start sequential recommendation, where new users with very short interaction sequences come with time. We cast this problem as a few-shot learning problem and adopt a meta-learning approach to developing our solution. For our task, a major obstacle of effective knowledge transfer that is there exists significant characteristic divergence between old and new interaction sequences for meta-learning. To address the above issues, we purpose a Multimodal MetaLearning (denoted as MML) approach that incorporates multimodal side information of items (e.g., text and image) into the meta-learning process, to stabilize and improve the meta-learning process for cold-start sequential recommendation. In specific, we design a group of multimodal meta-learners corresponding to each kind of modality, where ID features are used to develop the main meta-learner and the rest text and image features are used to develop auxiliary meta-learners. Instead of simply combing the predictions from different meta-learners, we design an adaptive, learnable fusion layer to integrate the predictions based on different modalities. Meanwhile, we design a cold-start item embedding generator, which utilize multimodal side information to warm up the ID embeddings of new items. Extensive offline and online experiments demonstrate that MML can significantly improve the recommendation performance for cold-start users compared with baseline models. Our code is released at https://github.com/RUCAIBox/MML.","PeriodicalId":389624,"journal":{"name":"Proceedings of the 31st ACM International Conference on Information & Knowledge Management","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124087412","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