Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining最新文献_第8页

ST-GSP: Spatial-Temporal Global Semantic Representation Learning for Urban Flow Prediction ST-GSP:面向城市流量预测的时空全局语义表征学习

Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining Pub Date : 2022-02-11 DOI: 10.1145/3488560.3498444

Liang Zhao, Min Gao, Zongwei Wang

{"title":"ST-GSP: Spatial-Temporal Global Semantic Representation Learning for Urban Flow Prediction","authors":"Liang Zhao, Min Gao, Zongwei Wang","doi":"10.1145/3488560.3498444","DOIUrl":"https://doi.org/10.1145/3488560.3498444","url":null,"abstract":"Urban flow prediction plays a crucial role in public transportation management and smart city construction. Although previous studies have achieved success in integrating spatial-temporal information to some extents, those models lack thoughtful consideration on global information and positional information in the temporal dimension, which can be summarized by three aspects: a) The models do not consider the relative position information of time axis, resulting in that the position features of flow maps are not effectively learned. b) They overlook the correlation among temporal dependencies of different scales, which lead to inaccurate global information representation. c) Those models only predict the flow map at the end of time sequence other than more flow maps before that, which results in neglecting parts of temporal features in the learning process. To solve the problems, we propose a novel model, Spatial-Temporal Global Semantic representation learning for urban flow Prediction (ST-GSP) in this paper. Specifically, for a), we design a semantic flow encoder that extracts relative positional information of time. Besides, the encoder captures the spatial dependencies and external factors of urban flow at each time interval. For b), we model the correlation among temporal dependencies of different scales simultaneously by using the multi-head self-attention mechanism, which can learn the global temporal dependencies. For c), inspired by the idea of self-supervised learning, we mask an urban flow map on the time sequence and predict it to pre-train a deep bidirectional learning model to catch the representation from its context. We conduct extensive experiments on two types of urban flows in Beijing and New York City to show that the proposed method outperforms state-of-the-art methods.","PeriodicalId":348686,"journal":{"name":"Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134410990","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: 13

Dy-HIEN: Dynamic Evolution based Deep Hierarchical Intention Network for Membership Prediction 基于动态演化的深度层次意向网络成员预测

Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining Pub Date : 2022-02-11 DOI: 10.1145/3488560.3498517

Zhenyun Hao, Jianing Hao, Zhaohui Peng, Senzhang Wang, Philip S. Yu, Xue Wang, Jian Wang

{"title":"Dy-HIEN: Dynamic Evolution based Deep Hierarchical Intention Network for Membership Prediction","authors":"Zhenyun Hao, Jianing Hao, Zhaohui Peng, Senzhang Wang, Philip S. Yu, Xue Wang, Jian Wang","doi":"10.1145/3488560.3498517","DOIUrl":"https://doi.org/10.1145/3488560.3498517","url":null,"abstract":"Many video websites offer packages composed of paid videos. Users who purchase a package become members of the website, and thus can enjoy the membership service, such as watching the paid videos. It is practically important to predict which users will become members so that the website can recommend them the suitable packages for purchasing. Existing works generally predict the purchase behavior of users through capturing their interests in items. However, such works cannot be directly applied to the studied problem due to the following challenges. First, some important features of videos and packages change over time, such as the number of clicks and the update of the videos. Existing methods are not capable to capture such dynamic features. Second, a user's purchasing intention is very hard to capture. A user watching a video does not necessarily mean that he/she would like to purchase the corresponding package. In this paper, we propose a Dynamic Evolution based Deep Hierarchical Intention Network (Dy-HIEN for short) for membership prediction, which contains two modules. In the first module, we design a dynamic embedding learning method, applying multi-relational heterogeneous information network and attention mechanism to effectively represent the embedding of videos and packages. In the second module, a hierarchical method is proposed to extract the purchase intention of users. First, the video play history is divided into sessions based on the clicks on packages, and then time-order encoder and kernel functions are applied to mine the intention pattern associated with the package clicked in each session. Extensive experiments on real-world datasets are conducted to demonstrate the advantages of the proposed model on a variety of evaluation metrics.","PeriodicalId":348686,"journal":{"name":"Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134455418","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

A Counterfactual Modeling Framework for Churn Prediction 流失预测的反事实建模框架

Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining Pub Date : 2022-02-11 DOI: 10.1145/3488560.3498468

Guozhen Zhang, Jinwei Zeng, Zhengyue Zhao, Depeng Jin, Yong Li

{"title":"A Counterfactual Modeling Framework for Churn Prediction","authors":"Guozhen Zhang, Jinwei Zeng, Zhengyue Zhao, Depeng Jin, Yong Li","doi":"10.1145/3488560.3498468","DOIUrl":"https://doi.org/10.1145/3488560.3498468","url":null,"abstract":"Accurate churn prediction for retaining users is keenly important for online services because it determines their survival and prosperity. Recent research has specified social influence to be one of the most important reasons for user churn, and thereby many works start to model its effects on user churn to improve the prediction performance. However, existing works only use the data's correlational information while neglecting the problem's causal nature. Specifically, the fact that a user's churn is correlated with some social factors does not mean he/she is actually influenced by his/her friends, which results in inaccurate and unexplainable predictions of the existing methods. To bridge this gap, we develop a counterfactual modeling framework for churn prediction, which can effectively capture the causal information of social influence for accurate and explainable churn predictions. Specifically, we first propose a backbone framework that uses two separate embeddings to model users' endogenous churn intentions and the exogenous social influence. Then, we propose a counterfactual data augmentation module to introduce the causal information to the model by providing partially labeled counterfactual data. Finally, we design a three-headed counterfactual prediction framework to guide the model to learn causal information to facilitate churn prediction. Extensive experiments on two large-scale datasets with different types of social relations show our model's superior prediction performance compared with the state-of-the-art baselines. We further conduct an in-depth analysis of the prediction results demonstrating our proposed method's ability to capture causal information of social influence and give explainable churn predictions, which provide insights into designing better user retention strategies.","PeriodicalId":348686,"journal":{"name":"Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132682722","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

Graph Neural Networks for the Global Economy with Microsoft DeepGraph 图神经网络的全球经济与微软DeepGraph

Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining Pub Date : 2022-02-11 DOI: 10.1145/3488560.3510020

Jaewon Yang, Baoxu Shi, A. Samylkin

{"title":"Graph Neural Networks for the Global Economy with Microsoft DeepGraph","authors":"Jaewon Yang, Baoxu Shi, A. Samylkin","doi":"10.1145/3488560.3510020","DOIUrl":"https://doi.org/10.1145/3488560.3510020","url":null,"abstract":"Graph Neural Networks (GNNs) are AI models that learn embeddings for the nodes in a graph and use the embeddings to perform prediction tasks. In this talk, we present how we developed GNNs for the LinkedIn economic graph. LinkedIn economic graph is a digital representation of the global economy with 1B nodes and 200B edges, consisting of social graphs about members' connections, activity graphs between members and other economic entities, and knowledge graphs about members', companies', job postings' attributes. By applying GNN to this graph, we can utilize the full potential of the economic graph in many search and recommendation products across LinkedIn. The biggest challenge was to scale up GNNs to a massive scale of billion nodes and edges. To address this challenge, we developed Microsoft DeepGraph, an open source library for large scale GNN development. DeepGraph allows for training GNNs on large graphs by serving the graph in a distributed fashion with graph engine servers. In this talk, we will highlight the strengths of DeepGraph, such as support for both PyTorch and TensorFlow, and integration with Azure ML and Azure Kubernetes Service. We will share lessons and findings from developing GNNs for various applications around the LinkedIn economic graph. We will explain how we combine graphs such as social graph, activity graph, knowledge graphs into one gigantic heterogeneous graph, and what algorithms we employed for this heterogenous graph. We will present a few case studies, such as how we identify job postings with vague titles and replace them with more specific titles using GNNs.","PeriodicalId":348686,"journal":{"name":"Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129692478","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

AngHNE AngHNE

Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining Pub Date : 2022-02-11 DOI: 10.1145/3488560.3498510

Cangqi Zhou, Hui Chen, Jing Zhang, Qianmu Li, Dianming Hu

{"title":"AngHNE","authors":"Cangqi Zhou, Hui Chen, Jing Zhang, Qianmu Li, Dianming Hu","doi":"10.1145/3488560.3498510","DOIUrl":"https://doi.org/10.1145/3488560.3498510","url":null,"abstract":"Real-world networks often show heterogeneity. A frequently encountered type is the bipartite heterogeneous structure, in which two types of nodes and three types of edges exist. Recently, much attention has been devoted to representation learning in these networks. One of the essential differences between heterogeneous and homogeneous learning is that the former structure requires methods to possess awareness to node and edge types. Most existing methods, including metapath-based, proximity-based and graph neural network-based, adopt inner product or vector norms to evaluate the similarities in embedding space. However, these measures either violates the triangle inequality, or show severe sensitivity to scaling transformation. The limitations often hinder the applicability to real-world problems. In view of this, in this paper, we propose a novel angle-based method for bipartite heterogeneous network representation. Specifically, we first construct training sets by generating quintuples, which contain both positive and negative samples from two different parts of networks. Then we analyze the quintuple-based problem from a geometry perspective, and transform the comparisons between preferred and non-preferred samples to the comparisons of angles. In addition, we utilize convolution modules to extract node features. A hinge loss, as the final objective, is proposed to relax the angular constraint for learning. Extensive experiments for two typical tasks show the efficacy of the proposed method, comparing with eight competitive methods.","PeriodicalId":348686,"journal":{"name":"Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining","volume":"305 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114958087","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

Graph Minimally-supervised Learning 图最小监督学习

Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining Pub Date : 2022-02-11 DOI: 10.1145/3488560.3501390

Kaize Ding, Jundong Li, N. Chawla, Huan Liu

{"title":"Graph Minimally-supervised Learning","authors":"Kaize Ding, Jundong Li, N. Chawla, Huan Liu","doi":"10.1145/3488560.3501390","DOIUrl":"https://doi.org/10.1145/3488560.3501390","url":null,"abstract":"Graphs are widely used for abstracting complex systems of interacting objects, such as social networks, knowledge graphs, and traffic networks, as well as for modeling molecules, manifolds, and source code. To model such graph-structured data, graph learning, in particular deep graph learning with graph neural networks, has drawn much attention in both academic and industrial communities lately. Prevailing graph learning methods usually rely on learning from \"big'' data, requiring a large amount of labeled data for model training. However, it is common that graphs are associated with \"small'' labeled data as data annotation and labeling on graphs is always time and resource-consuming. Therefore, it is imperative to investigate graph learning with minimal human supervision for the low-resource settings where limited or even no labeled data is available. In this tutorial, we will focus on the state-of-the-art techniques of Graph Minimally-supervised Learning, in particular a series of weakly-supervised learning, few-shot learning, and self-supervised learning methods on graph-structured data as well as their real-world applications. The objectives of this tutorial are to: (1) formally categorize the problems in graph minimally-supervised learning and discuss the challenges under different learning scenarios; (2) comprehensively review the existing and recent advances of graph minimally-supervised learning; and (3) elucidate open questions and future research directions. This tutorial introduces major topics within minimally-supervised learning and offers a guide to a new frontier of graph learning. We believe this tutorial is beneficial to researchers and practitioners, allowing them to collaborate on graph learning.","PeriodicalId":348686,"journal":{"name":"Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115031559","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

AdaptKT: A Domain Adaptable Method for Knowledge Tracing AdaptKT:一种领域自适应的知识跟踪方法

Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining Pub Date : 2022-02-11 DOI: 10.1145/3488560.3498379

Song Cheng, Qi Liu, Enhong Chen, Kai Zhang, Zhenya Huang, Yu Yin, Xiaoqing Huang, Yu Su

{"title":"AdaptKT: A Domain Adaptable Method for Knowledge Tracing","authors":"Song Cheng, Qi Liu, Enhong Chen, Kai Zhang, Zhenya Huang, Yu Yin, Xiaoqing Huang, Yu Su","doi":"10.1145/3488560.3498379","DOIUrl":"https://doi.org/10.1145/3488560.3498379","url":null,"abstract":"Knowledge tracing is a crucial and fundamental task in online education systems, which can predict students' knowledge state for personalized learning. Unfortunately, existing methods are domain-specific, whereas there are many domains (e.g., subjects, schools) in the real education scene and some domains suffer from the problem of lacking sufficient data. Therefore, how to exploit the knowledge in other domains, to improve the model's performance for target domain remains pretty much open. We term this problem as Domain Adaptation for Knowledge Tracing (DAKT), which aims to transfer knowledge from the source domain to the target one for knowledge tracing. In this paper, we propose a novel adaptable method, namely Adaptable Knowledge Tracing (AdaptKT), which contains three phases to explore this problem. Specifically, phase I is instance selection. Given the question texts of two domains, we train an auto-encoder to select and embed similar instances from both domains. Phase II is distribution discrepancy minimizing. After obtaining the selected instances and their linguistic representations, we train a knowledge tracing model and adopt the Maximum Mean Discrepancy (MMD) to minimize the discrepancy between the distributions of the domain-specific knowledge states. Phase III is fine-tuning of the output layer. We replace the output layer of the model that trained in phase II by a new one to make the knowledge tracing model's output dimension matches the number of knowledge concepts in the target domain. The new output layer is trained while other parameters that before it are frozen. We conduct extensive experiments on two large-scale real-world datasets, where the experimental results clearly demonstrate the effectiveness of AdaptKT for solving DAKT problem. We will public the code on the Github after the acceptance of the paper.","PeriodicalId":348686,"journal":{"name":"Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining","volume":"95 3","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"113981352","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

Doctoral Consortium of WSDM'22: Exploring the Bias of Adversarial Defenses WSDM'22博士联盟:探索对抗性防御的偏见

Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining Pub Date : 2022-02-11 DOI: 10.1145/3488560.3502215

Han Xu

引用次数: 0

a2RegInf: An Interactive System for Maximizing Influence within Arbitrary Number of Arbitrary Shaped Query Regions reginf:在任意数量的任意形状查询区域内最大化影响的交互式系统

Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining Pub Date : 2022-02-11 DOI: 10.1145/3488560.3502188

Hui Li, Q. Yang, Jiangtao Cui

引用次数: 0

MtCut: A Multi-Task Framework for Ranked List Truncation MtCut:排序列表截断的多任务框架

Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining Pub Date : 2022-02-11 DOI: 10.1145/3488560.3498466

Dong Wang, Jianxin Li, Tianchen Zhu, Haoyi Zhou, Qishan Zhu, Yuxin Wen, Hongming Piao

引用次数: 1