OpenRaiser/Intern-Atlas huggingface dataset:
Multi-hop Reading on Memory Neural Network with Selective Coverage for Medication Recommendation
Authors: Yanda Wang, Weitong Chen, Dechang Pi, Lin Yue, Miao Xu, Xue Li
Venue: CIKM
Year: 2021

Medication recommendation aiming at accurate prescription is a significant clinical application that assists caregivers in professional practice of medicine, and obtaining informative patient representations plays an important role in building effective recommendation models. Meanwhile, conducting attentive multi-hop reading on Memory Neural Network (MemNN) that stores knowledge from previous admissions is widely applied to derive contextual patterns for accurate patient representations. However, regular attentive reading may repeatedly attend to the same slots of MemNN. Although the coverage mechanism is proposed to tackle the problem, it is based on the assumption that there is one-to-one alignment between source information and target outputs, which medical records do not follow. In pursuit of a valuable model for medication recommendation, we propose the Multi-hop Reading with Selective Coverage (MRSC). MRSC firstly conducts information selection on MemNN based on the coverage of each slot. Then the method involves coverage into the attention calculation during the multi-hop reading on MemNN, making sure that all important historical records is fully utilized by balancing attention within selected information. Experiments on real-world clinical dataset demonstrate that MRSC successfully derives informative patient representations for the recommendation by conducting selection on MemNN and limiting attention adjustment within selected information.
Popularity-Enhanced News Recommendation with Multi-View Interest Representation
Authors: Jingkun Wang, Yipu Chen, Zichun Wang, Wen Zhao
Venue: CIKM
Year: 2021

News recommendation is of vital importance to alleviating in-formation overload. Recent research shows that precise modeling of news content and user interests become critical for news rec-ommendation. Existing methods usually utilize information such as news title, abstract, entities to predict Click Through Rate(CTR) or add some auxiliary tasks to a multi-task learning framework. However, none of them directly consider predicted news popularity and the degree of users' attention to popular news into the CTR prediction results. Meanwhile, multiple inter-ests may arise throughout users' browsing history. Thus it is hard to represent user interests via a single user vector. In this paper, we propose PENR, a Popularity-Enhanced News Recommenda-tion method, which integrates popularity prediction task to im-prove the performance of the news encoder. News popularity score is predicted and added to the final CTR, while news popu-larity is utilized to model the degree of users' tendency to follow hot news. Moreover, user interests are modeled from different perspectives via a subspace projection method that assembles the browsing history to multiple subspaces. In this way, we capture users' multi-view interest representations. Experiments on a real-world dataset validate the effectiveness of our PENR approach.
Tree Decomposed Graph Neural Network
Authors: Yu Wang, Tyler Derr
Venue: CIKM
Year: 2021

Graph Neural Networks (GNNs) have achieved significant success in learning better representations by performing feature propagation and transformation iteratively to leverage neighborhood information. Nevertheless, iterative propagation restricts the information of higher-layer neighborhoods to be transported through and fused with the lower-layer neighborhoods', which unavoidably results in feature smoothing between neighborhoods in different layers and can thus compromise the performance, especially on heterophily networks. Furthermore, most deep GNNs only recognize the importance of higher-layer neighborhoods while yet to fully explore the importance of multi-hop dependency within the context of different layer neighborhoods in learning better representations. In this work, we first theoretically analyze the feature smoothing between neighborhoods in different layers and empirically demonstrate the variance of the homophily level across neighborhoods at different layers. Motivated by these analyses, we further propose a tree decomposition method to disentangle neighborhoods in different layers to alleviate feature smoothing among these layers. Moreover, we characterize the multi-hop dependency via graph diffusion within our tree decomposition formulation to construct Tree Decomposed Graph Neural Network (TDGNN), which can flexibly incorporate information from large receptive fields and aggregate this information utilizing the multi-hop dependency. Comprehensive experiments demonstrate the superior performance of TDGNN on both homophily and heterophily networks under a variety of node classification settings. Extensive parameter analysis highlights the ability of TDGNN to prevent over-smoothing and incorporate features from shallow layers with deeper multi-hop dependencies, which provides new insights towards deeper graph neural networks.
An Efficient Quantitative Approach for Optimizing Convolutional Neural Networks
Authors: Yuke Wang, Boyuan Feng, Xueqiao Peng, Yufei Ding
Venue: CIKM
Year: 2021

With the increasing popularity of deep learning, Convolutional Neural Networks (CNNs) have been widely applied in various domains, such as image classification and object detection, and achieve stunning success in terms of their high accuracy over the traditional statistical methods. To exploit the potentials of CNN models, a huge amount of research and industry efforts have been devoted to optimizing CNNs. Among these endeavors, CNN architecture design has attracted tremendous attention because of its great potential of improving model accuracy or reducing model complexity. However, existing work either introduces repeated training overhead in the search process or lacks an interpretable metric to guide the design. To clear these hurdles, we propose 3D-Receptive Field (3DRF), an explainable and easy-to-compute metric, to estimate the quality of a CNN architecture and guide the search process of designs. To validate the effectiveness of 3DRF, we build a static optimizer to improve the CNN architectures at both the stage level and the kernel level. Our optimizer not only provides a clear and reproducible procedure but also mitigates unnecessary training efforts in the architecture search process. Extensive experiments and studies show that the models generated by our optimizer can achieve up to 5.47% accuracy improvement and up to 65.38% parameters deduction, compared with state-of-the-art CNN structures like MobileNet and ResNet.
Reliable and Privacy-Preserving Task Matching in Blockchain-Based Crowdsourcing
Authors: Baolai Wang, Shaojing Fu, Xuyun Zhang, Tao Xie, Lingjuan Lyu, Yuchuan Luo
Venue: CIKM
Year: 2021

With the number of users in crowdsourcing increasing rapidly, task matching service is attracting more and more attention. However, it also causes many security concerns, one of which is the leakage of sensitive information. Privacy-preserving task matching techniques can protect the private information of task requesters and workers. Whereas existing privacy-preserving task matching schemes are constructed on a central server, and thereby they may suffer from potential wrongdoings of a malicious server. In addition, most of them only provide accurate task matching, which means that they cannot tolerate keyword spelling errors, leading to the decline of task matching accuracy. In this paper, we propose a Reliable and Privacy-preserving Task Matching scheme (RPTM) for crowdsourcing. To guarantee the reliability of task matching results, RPTM employs smart contracts to ensure that operations of RPTM are faithfully performed. However, it may still disclose the privacy of users due to the transparency of the blockchain. In order to deal with this problem, RPTM can perform task matching service without compromising the privacy of task requesters and workers by leveraging a novel integer vector encryption scheme. Moreover, RPTM supports multi-keyword fuzzy matching by exploiting locality sensitive hashing and Bloom filter, which can tolerate keyword spelling errors and different expression formats. Extensive analysis and experiments based on a test net of EOS show that RPTM is efficient and secure.
A Cohesive Structure Based Bipartite Graph Analytics System
Authors: Kai Wang, Yiheng Hu, Xuemin Lin, Wenjie Zhang, Lu Qin, Ying Zhang
Venue: CIKM
Year: 2021

Bipartite graphs arise naturally when modeling two different types of entities such as user-item, author-paper, and director-board. In recent years, driven by numerous real-world applications in these networks, mining cohesive structures in bipartite graphs becomes a popular research topic. In this paper, we propose the first cohesive-structure-based bipartite graph analytics system, CohBGA. The key innovative features of our system are as follows. Firstly, we involve several cohesive-structure-based models and statistics in our system to analyze bipartite graphs at different levels of granularity. Secondly, CohBGA has a user-friendly and interactive visual interface with various functional tools to meet users' diverse query requirements. Thirdly, we implement state-of-the-art algorithms in CohBGA to support efficient query processing. Furthermore, as a generic framework is designed in CohBGA, CohBGA is going to be an open-source bipartite graph analytics platform that allows researchers to evaluate the effectiveness of more cohesive-structure-based models and algorithms for bipartite graphs.
REFORM: Error-Aware Few-Shot Knowledge Graph Completion
Authors: Song Wang, Xiao Huang, Chen Chen, Liang Wu, Jundong Li
Venue: CIKM
Year: 2021

Knowledge graphs (KGs) are of great importance in various artificial intelligence systems, such as question answering, relation extraction, and recommendation. Nevertheless, most real-world KGs are highly incomplete, with many missing relations between entities. To discover new triples (i.e., head entity, relation, tail entity), many KG completion algorithms have been proposed in recent years. However, a vast majority of existing studies often require a large number of training triples for each relation, which contradicts the fact that the frequency distribution of relations in KGs often follows a long tail distribution, meaning a majority of relations have only very few triples. Meanwhile, since most existing large-scale KGs are constructed automatically by extracting information from crowd-sourcing data using heuristic algorithms, plenty of errors could be inevitably incorporated due to the lack of human verification, which greatly reduces the performance for KG completion. To tackle the aforementioned issues, in this paper, we study a novel problem of error-aware few-shot KG completion and present a principled KG completion framework REFORM. Specifically, we formulate the problem under the few-shot learning framework, and our goal is to accumulate meta-knowledge across different meta-tasks and generalize the accumulated knowledge to the meta-test task for error-aware few-shot KG completion. To address the associated challenges resulting from insufficient training samples and inevitable errors, we propose three essential modules neighbor encoder, cross-relation aggregation, and error mitigation in each meta-task. Extensive experiments on three widely used KG datasets demonstrate the superiority of the proposed framework REFORM over competitive baseline methods.
Spatio-Temporal-Categorical Graph Neural Networks for Fine-Grained Multi-Incident Co-Prediction
Authors: Zhaonan Wang, Renhe Jiang, Zekun Cai, Zipei Fan, Xin Liu, Kyoung-Sook Kim, Xuan Song, Ryosuke Shibasaki
Venue: CIKM
Year: 2021

Forecasting incident occurrences (e.g. crime, EMS, traffic accident) is a crucial task for emergency service providers and transportation agencies in performing response time optimization and dynamic fleet management. However, such events are by nature rare and sparse, which causes the label imbalance problem and inferior performance of models relying on data sufficiency. The existing studies circumvent, instead of truly solving, this issue by defining the incident prediction problem in a coarse-grained temporal (e.g. daily) setting, which leaves the proposed models unrobust to fine-grained dynamics and trivial for the real-world decision making. In this paper, we tackle the temporally fine-grained incident prediction problem in a sparse setting by explicitly exploiting the behind-the-scene chainlike triggering mechanism. Moreover, this chain effect roots in multiple domains (i.e. spatial, categorical), which further entangles with the temporal dimension and happens to be time-variant. To be specific, we propose a novel deep learning framework, namely Spatio-Temporal-Categorical Graph Neural Networks (STC-GNN), to handle the multidimensional and dynamic chain effect for performing fine-grained multi-incident co-prediction. Extensive experiments on three real-world city-level incident datasets verify the insightfulness of our perspective and effectiveness of the proposed model.
Fulfillment-Time-Aware Personalized Ranking for On-Demand Food Recommendation
Authors: Haishuai Wang, Zhao Li, Xuanwu Liu, Donghui Ding, Zehong Hu, Peng Zhang, Chuan Zhou, Jiajun Bu
Venue: CIKM
Year: 2021

On-demand food delivery (OFD) platforms have greatly impacted the food service industry, where OFD recommendation systems play a central role in enhancing user experience and raising revenues. OFD recommendation, compared with existing online e-commerce recommendation systems, needs to put more emphasis on fulfillment time related variables, because the order fulfillment cycle time (OFCT) which refers to the time elapsed between a user placing a food order and receiving the food significantly influences a user's choice from the recommended items. In this paper, we investigate the OFCT related information and propose a Fulfillment-Time-Aware Personalized Ranking (FTAPR) method for recommendation. FTAPR mainly consists of three components. First, Transformers are used to estimate OFCT based on a large amount of user order sequences. Then, the predicted OFCT and other OFCT related features are fused and encoded by a deep & cross network to learn fulfillment time related feature representation. At the last step, the time bias representation from the deep & cross network is integrated into the ranking system to deliver final search results. Extensive offline and online experiments on real-world datasets collected from one of China's largest OFD platforms Ele.me show the superiority of our model, e.g., an online A/B testing shows that FTAPR brings 1.3% and 2.5% gains in CTR and CVR compared with baselines.
Addressing the Hardness of k-Facility Relocation Problem: A Pair of Approximate Solutions
Authors: Hu Wang, Hui Li, Meng Wang, Jiangtao Cui
Venue: CIKM
Year: 2021

Facility Relocation (FR), which is an effort to reallocate the placement of facilities to adapt to the changes of urban planning and population distribution, has remarkable impact on many application areas. Existing solutions to the FR problem either focus on relocating one facility (ie 1-FR) or fail to guarantee the result quality on relocating k>1 facilities (ie k-FR). As k-FR problem is NP-hard and is not submodular or non-decreasing, traditional hill-climb approximate algorithm cannot be directly applied. In light of that, we propose to transform k-FR into another facility placement problem, which is submodular and non-decreasing. We theoretically prove that the optimal solution of both problems are equivalent. Accordingly, we are able to present the first approximate solution towards the k-FR, namely FR2FP. Our extensive comparison over both FR2FP and the state-of-the-art heuristic solution shows that FR2FP, although provides approximation guarantee, cannot necessarily given superior results to the heuristic solution. The comparison motivates and, more importantly, directs us to present an advanced approximate solution, namely FR2FP-ex. Extensive experimental study over both real-world and synthetic datasets have verified that, FR2FP-ex demonstrates the best result quality. In addition, we also exactly unveil the scenarios when the state-of-the-art heuristic would fail to provide satisfied results in practice.
Modeling Heterogeneous Graph Network on Fraud Detection: A Community-based Framework with Attention Mechanism
Authors: Li Wang, Peipei Li, Kai Xiong, Jiashu Zhao, Rui Lin
Venue: CIKM
Year: 2021

Fraud activities in e-commerce, such as spam reviews and fake shopping behaviors, significantly mislead customers' decision making, damage the platforms' reputation, and reduce enterprises' revenue. In recent years, GNN-based models have been widely adopted in fraud detection tasks, which have shown better performance compared to conventional rule-based methods and feature-based models. Most GNN-based models focus on homogeneous graphs, usually including user-to-user, or item-to-item connections. These types of graphs have limitations of eliminating certain types of connections, such as user-item connections. In addition, GNN-based models aggregate neighborhood information based on the assumption that neighbors share the similar structure and content. However, in fraud detection tasks, two major inconsistency issues arise: Severe mixture of structure-inconsistency due to extremely unbalanced positive and negative samples; and mixture of content-inconsistency due to the difference between various item categories. To address the above issues, we propose a Community-based Framework with ATtention mechanism for large-scale Heterogeneous graphs (C-FATH). In order to utilize the entire heterogeneous graph, we directly model on the heterogeneous graph and combine it with homogeneous graphs. The structure-inconsistent nodes are filtered by introducing the community information when constructing neighbors. Content-inconsistent nodes are selected with lower probability by a similarity-based sampling strategy. Further, the model is trained in a multi-task manner that each node type (e.g. user, item, device, order, and review) is associated with a specific loss function. Comprehensive experiments are conducted on two public review datasets and two large-scale datasets from JD.com, and the experimental results demonstrate the effectiveness and scalability of the proposed C-FATH compared to the state-of-the-art approaches.
Modeling Inter-Claim Interactions for Verifying Multiple Claims
Authors: Shuai Wang, Wenji Mao
Venue: CIKM
Year: 2021

To inhibit the spread of rumorous information, fact checking aims at retrieving evidence to verify the truthfulness of a given statement. Fact checking methods typically use knowledge graphs (KGs) as external repositories and develop reasoning methods to retrieve evidence from KGs. As real-world statement is often complex and contains multiple claims, multi-claim fact verification is not only necessary but more important for practical applications. However, existing methods only focus on verifying a single claim (i.e. a single-claim statement). Multiple claims imply rich context information and modeling the interrelations between claims can facilitate better verification of a multi-claim statement as a whole. In this paper, we propose a computational method to model inter-claim interactions for multi-claim fact checking. To focus on relevant claims within a statement, our method first extracts topics from the statement and connects the triple claims in the statement to form a claim graph. It then learns a policy-based agent to sequentially select topic-related triples from the claim graph. To fully exploit information from the statement, our method further employs multiple agents and develops a hierarchical attention mechanism to verify multiple claims as a whole. Experimental results on two real-world datasets show the effectiveness of our method for multi-claim fact verification.
'Could You Describe the Reason for the Transfer?': A Reinforcement Learning Based Voice-Enabled Bot Protecting Customers from Financial Frauds
Authors: Zihao Wang, Fudong Wang, Haipeng Zhang, Minghui Yang, Shaosheng Cao, Zujie Wen, Zhe Zhang
Venue: CIKM
Year: 2021

With the booming of the Internet finance and e-payment business, telecom and online fraud has become a serious problem which grows rapidly. In China, 351 billion RMB (approximately 0.3% of China's GDP) was lost in 2018 due to telecommunication and online fraud, influencing tens of millions of individual customers. Anti-fraud algorithms have been widely adopted by major Internet finance companies to detect and block transactions induced by scam. However, due to limited contextual information, most systems would probably mistakenly block the normal transactions, leading to poor user experience. On the other hand, if the transactions induced by scam are detected yet not fully explained to the users, the users will continue to pay, suffering from direct financial losses. To address these problems, we design a voice-enabled bot that interacts with the customers who are involved with potential telecommunication and online frauds decided by the back-end system. The bot seeks additional information from the customers through natural conversations to confirm whether the customers are scammed and identify the actual fraud types. The details about the frauds are then provided to convince the customers that they are on the edge of being scammed. Our bot adopts offline reinforcement learning (RL) to learn dialogue policies from real-world human-human chat logs. During the conversations, our bot also identifies fraud types every turn based on the dialogue state. The bot proposed outperforms baseline dialogue strategies by 2.8% in terms of task success rate, and 5% in terms of dialogue accuracy in offline evaluations. Furthermore, in the 8 months of real-world deployment, our bot lowers the dissatisfaction rate by 25% and increases the fraud prevention rate by 135% relatively, indicating a significant improvement in user experience as well as anti-fraud effectiveness. More importantly, we help prevent millions of users from being deceived, and avoid trillions of financial losses.
Behind the Scenes: An Exploration of Trigger Biases Problem in Few-Shot Event Classification
Authors: Peiyi Wang, Runxin Xu, Tianyu Liu, Damai Dai, Baobao Chang, Zhifang Sui
Venue: CIKM
Year: 2021

Few-Shot Event Classification (FSEC) aims at developing a model for event prediction, which can generalize to new event types with a limited number of annotated data. Existing FSEC studies have achieved high accuracy on different benchmarks. However, we find they suffer from trigger biases that signify the statistical homogeneity between some trigger words and target event types, which we summarize as trigger overlapping and trigger separability. The biases can result in context-bypassing problem, i.e., correct classifications can be gained by looking at only the trigger words while ignoring the entire context. Therefore, existing models can be weak in generalizing to unseen data in real scenarios. To further uncover the trigger biases and assess the generalization ability of the models, we propose two new sampling methods, Trigger-Uniform Sampling (TUS) and COnfusion Sampling (COS), for the meta tasks construction during evaluation. Besides, to cope with the context-bypassing problem in FSEC models, we introduce adversarial training and trigger reconstruction techniques. Experiments show these techniques help not only improve the performance, but also enhance the generalization ability of models.