OpenRaiser/Intern-Atlas huggingface dataset:
PRID: An Efficient Pub/Sub Ride Hitching System
Authors: Yafei Li, Lei Gao, Haobo Sun, Huiling Li, Qingshun Wu
Venue: CIKM
Year: 2022

As a convenient, economical and low carbon transport mode, ride hitching has gained tremendously prevalent in the globe. This paper presents PRID that offers an efficient ride hitching service with sufficient results analysis. In general, PRID is implemented by a publish/subscribe service, which provides a user-friendly web browser client that allows users (e.g., company managers and data analysts) to perform the performance analysis for ride hitching through simple interactive operations. Moreover, the server side of the PRID system consists of three modules including a top-k initialization module, an order dispatch module, and a top-k maintenance module, which can process the matching between drivers and riders efficiently. PRID system aims at continuously maintaining the update-to-date k best matched ride orders for each driver with less time cost, and monitoring their matching efficiency and effectiveness meanwhile. Finally, we demonstrate the PRID system on a real-world dataset, showing its good performance. The source code of the demonstration has been shared on https://github.com/wuqingshun/PRID.
Dual-Augment Graph Neural Network for Fraud Detection
Authors: Qiutong Li, Yanshen He, Cong Xu, Feng Wu, Jianliang Gao, Zhao Li
Venue: CIKM
Year: 2022

Graph Neural Networks (GNNs) have drawn attention due to their excellent performance in fraud detection tasks, which reveal fraudsters by aggregating the features of their neighbors. However, some fraudsters typically tend to alleviate their suspiciousness by connecting with many benign ones. Besides, label-imbalanced neighborhood also deteriorates fraud detection accuracy. Such behaviors violate the homophily assumption and worsen the performance of GNN-based fraud detectors. In this paper, we propose a Dual-Augment Graph Neural Network (DAGNN) for fraud detection tasks. In DAGNN, we design a two-pathway framework including disparity augment (DA) pathway and similarity augment (SA) pathway. Accordingly, we devise two novel information aggregation strategies. One is to augment the disparity between target node and its heterogenous neighbors in original topology. The other is to augment its similarity to homogenous neighbors in a relatively label-balanced neighborhood. The experimental results compared with the state-of-the-art models on two real-world datasets demonstrate the superiority of the proposed DAGNN.
SPOT: Knowledge-Enhanced Language Representations for Information Extraction
Authors: Jiacheng Li, Yannis Katsis, Tyler Baldwin, Ho-Cheol Kim, Andrew Bartko, Julian J. McAuley, Chun-Nan Hsu
Venue: CIKM
Year: 2022

Knowledge-enhanced pre-trained models for language representation have been shown to be more effective in knowledge base construction tasks (i.e.,~relation extraction) than language models such as BERT. These knowledge-enhanced language models incorporate knowledge into pre-training to generate representations of entities or relationships. However, existing methods typically represent each entity with a separate embedding. As a result, these methods struggle to represent out-of-vocabulary entities and a large amount of parameters, on top of their underlying token models (i.e., the transformer), must be used and the number of entities that can be handled is limited in practice due to memory constraints. Moreover, existing models still struggle to represent entities and relationships simultaneously. To address these problems, we propose a new pre-trained model that learns representations of both entities and relationships from token spans and span pairs in the text respectively. By encoding spans efficiently with span modules, our model can represent both entities and their relationships but requires fewer parameters than existing models. We pre-trained our model with the knowledge graph extracted from Wikipedia and test it on a broad range of supervised and unsupervised information extraction tasks. Results show that our model learns better representations for both entities and relationships than baselines, while in supervised settings, fine-tuning our model outperforms RoBERTa consistently and achieves competitive results on information extraction tasks.
Dynamic Network Embedding via Temporal Path Adjacency Matrix Factorization
Authors: Zhuoming Li, Darong Lai
Venue: CIKM
Year: 2022

Network embedding has been widely investigated to learn low dimensional nodes representation of networks, and serves for many downstream machine learning tasks. Previous network embedding studies mainly focus on static networks, and cannot adapt well to the characteristics of dynamic networks which are evolving over time. Some works on dynamic network embedding have tried to improve the computation efficiency for incremental updates of embedding vectors, while others have made efforts to utilize temporal information to enhance the quality of embedding vectors. However, few existing works can fulfill both efficiency and quality requirements. In this article, a novel dynamic network embedding model named TPANE (Temporal Path Adjacency Matrix based Network Embedding) is proposed. It employs a new network proximity measure: Temporal Path Adjacency, which is capable of capturing the temporal dependency between edges as well as being incrementally computed in an efficient way. It evaluates the similarity between nodes via the count of temporal paths between them, rather than making random sampling approximation, and adopts matrix factorization to obtain embedding vectors. Link prediction experiments on various real-world dynamic networks have been conducted to show the superior performance of TPANE against other state-of-the-art methods. Time consumption analysis also shows that TPANE is more efficient in incremental updates.
CNewsTS - A Large-scale Chinese News Dataset with Hierarchical Topic Category and Summary
Authors: Quanzhi Li, Yingchi Liu, Yang Chao
Venue: CIKM
Year: 2022

In this paper, we present a large Chinese news article dataset with 4.4 million articles. These articles are obtained from different news channels and sources. They are labeled with multi-level topic categories, and some of them also have summaries. This is the first Chinese news dataset that has both hierarchical topic labels and article full texts. And it is also the largest Chinese news topic dataset. We describe the data collection, annotation and quality evaluation process. The basic statistics of the dataset, comparison with other datasets and benchmark experiments are also presented.
Query Rewriting in TaoBao Search
Authors: Sen Li, Fuyu Lv, Taiwei Jin, Guiyang Li, Yukun Zheng, Tao Zhuang, Qingwen Liu, Xiaoyi Zeng, James T. Kwok, Qianli Ma
Venue: CIKM
Year: 2022

In e-commerce search engines, query rewriting (QR) is a crucial technique that improves shopping experience by reducing the vocabulary gap between user queries and product catalog. Recent works have mainly adopted the generative paradigm. However, they hardly ensure high-quality generated rewrites and do not consider personalization, which leads to degraded search relevance. In this work, we present Contrastive Learning Enhanced Query Rewriting (CLE-QR), the solution used in Taobao product search. It uses a novel contrastive learning enhanced architecture based on "query retrieval-semantic relevance ranking-online ranking". It finds the rewrites from hundreds of millions of historical queries while considering relevance and personalization. Specifically, we first alleviate the representation degeneration problem during the query retrieval stage by using an unsupervised contrastive loss, and then further propose an interaction-aware matching method to find the beneficial and incremental candidates, thus improving the quality and relevance of candidate queries. We then present a relevance-oriented contrastive pre-training paradigm on the noisy user feedback data to improve semantic ranking performance. Finally, we rank these candidates online with the user profile to model personalization for the retrieval of more relevant products. We evaluate CLE-QR on Taobao Product Search, one of the largest e-commerce platforms in China. Significant metrics gains are observed in online A/B tests. CLE-QR has been deployed to our large-scale commercial retrieval system and serviced hundreds of millions of users since December 2021. We also introduce its online deployment scheme, and share practical lessons and optimization tricks of our lexical match system.
Cooperative Max-Pressure Enhanced Traffic Signal Control
Authors: Lin Li, Renbo Li, Yuquan Peng, Chuanming Huang, Jingling Yuan
Venue: CIKM
Year: 2022

Adaptive traffic signal control is an important and challenging real-world problem that fits well with the task framework of deep reinforcement learning. As one of the critical design elements, the environmental state plays a crucial role in traffic signal control decisions. The state definitions of most existing works mostly contain lane-level queue length, intersection phase, and other features. However, these works are heuristically designed in representing states. This results in highly sensitive and unstable performances of next actions. The paper proposes a Cooperative Max-Pressure enhanced State Learning for the traffic signal control (CMP-SL), which is inspired by the advanced pressure definition for an intersection in the transportation field to cope with this problem. First, our CMP-SL explicitly extends the cooperative max-pressure to the state definition of a target intersection, aiming to obtain accurate environment information by including the traffic pressures of surrounding intersections. From then on, a graph attention mechanism (GAT) is used to learn the state representation of the target intersection in our spatial-temporal state module. Second, since the state is coupled with the reward in reinforcement learning, our method takes the cooperative max-pressure of the target intersection into the reward definition. Furthermore, a temporal convolutional network (TCN) based sequence model is used to capture the historical state of traffic flow. And the historical spatial-temporal and the current spatial state features are concatenated into a DQN network to predict the Q value and generate each phase action. Finally, experiments with two real-world traffic datasets demonstrate that our method achieves shorter vehicle average times and higher network throughput than the state-of-the-art models.
℘-MinHash Algorithm for Continuous Probability Measures: Theory and Application to Machine Learning
Authors: Ping Li, Xiaoyun Li, Gennady Samorodnitsky
Venue: CIKM
Year: 2022

This paper studies the scale-invariant "probability Jaccard'' (ProbJ), noted as ℐ℘, which is another variant of weighted Jaccard similarity. The standard and commonly used Jaccard index is not invariant of data scaling. Thus, the probability Jaccard can be a potentially useful extension to probability distributions. Before our paper, the problem of hashing the ℐ℘ for continuous probability measures is an open problem, where rigorous definitions and analysis are still absent in literature. In our work, we solve this problem systematically and completely. Specifically, we formalize the definition of ℐ℘ in continuous measure space, and propose a general ℘-MinHash sampling algorithm which generates samples following any target distribution, and preserves ℐ℘ between two distributions by the hash collision. In addition, a refined early stopping rule is proposed under a practical boundedness assumption. We validate the theory through simulation and experiments, and demonstrate the application of our method in machine learning problems.
Do Simpler Statistical Methods Perform Better in Multivariate Long Sequence Time-Series Forecasting?
Authors: Hao Li, Jie Shao, Kewen Liao, Mingjian Tang
Venue: CIKM
Year: 2022

Long sequence time-series forecasting has become a central problem in multivariate time-series analysis due to its difficulty of consistently maintaining low prediction errors. Recent research has concentrated on developing large deep learning frameworks such as Informer and SCINet with remarkable results. However, these complex approaches were not benchmarked with simpler statistical methods and hence this part of the puzzle is missing for multivariate long sequence time-series forecasting (MLSTF). We investigate two simple statistical methods for MLSTF and provide analysis to indicate that linear regression owns a lower upper bound of error than deep learning methods and SNaive can act as an effective nonparametric method with unpredictable trends. Evaluations across six real-world datasets demonstrate that linear regression and SNaive are able to achieve state-of-the-art performance for MLSTF.
Multi-agent Transformer Networks for Multimodal Human Activity Recognition
Authors: Jingcheng Li, Lina Yao, Binghao Li, Xianzhi Wang, Claude Sammut
Venue: CIKM
Year: 2022

Human activity recognition has become an important challenge yet to resolve while also having promising benefits in various applications for years. Existing approaches have made great progress by applying deep-learning and attention-based methods. However, the deep learning-based approaches may not fully exploit the features to resolve multimodal human activity recognition tasks. Also, the potential of attention-based methods still has not been fully explored to better extract the multimodal spatial-temporal relationship and produce robust results. In this work, we propose Multi-agent Transformer Network (MATN), a multi-agent attention-based deep learning algorithm, to address the above issues in multimodal human activity recognition. We first design a unified representation learning layer to encode the multimodal data, which preprocesses the data in a generalized and efficient way. Then we develop a multimodal spatial-temporal transformer module that applies the attention mechanism to extract the salient spatial-temporal features. Finally, we use a multi-agent training module to collaboratively select the informative modalities and predict the activity labels. We have extensively conducted experiments to evaluate MATN's performance on two public multimodal human activity recognition datasets. The results show that our model has achieved competitive performance compared to the state-of-the-art approaches, which also demonstrates scalability, effectiveness, and robustness.
An Extreme Semi-supervised Framework Based on Transformer for Network Intrusion Detection
Authors: Yangmin Li, Xinhang Yuan, Wengen Li
Venue: CIKM
Year: 2022

Network intrusion detection (NID) aims to detect various network attacks and is an important task for guaranteeing network security. However, existing NID methods usually require a large amount of labeled data for training, which is impractical in many real application scenarios due to the high cost. To address this issue, we proposed an extreme semi-supervised framework based on transformer (ESet) for NID. ESeT first developed a multi-level feature extraction module to learn both packet-level byte encoded features and flow-level frequency domain features to enrich the information for detection. Then, during the semi-supervised learning, ESeT designed the dual-encoding transformer to fuse the extracted features for intrusion detection and introduced the credibility selector to reduce the negative impacts of incorrect pseudo-labeling of unlabeled data. The experiment results show that our model achieves excellent performance (F1-score: 97.60%) with only a small proportion of labeled data (1%) on CIC-IDS2017 and CSE-CIC-IDS2018 datasets.
Visual Exploration of Literature with Argo Scholar
Authors: Kevin Li, Haoyang Yang, Evan Montoya, Anish Upadhayay, Zhiyan Zhou, Jon Saad-Falcon, Duen Horng Chau
Venue: CIKM
Year: 2022

Discovering and making sense of relevant literature is fundamental in any scientific field. Visualization can aid this process; however, existing tools' adoption and impact are often constrained, such as by their reliance on small, curated paper datasets that quickly become outdated or a lack of support for personalized exploration. We introduce Argo Scholar, an open-source visualization tool designed for interactive exploration of literature and easy sharing of exploration results. Argo Scholar is tightly integrated with Semantic Scholar's live data of over 200 million papers, enabling users to generate personalized literature exploration results in real-time through flexible, incremental exploration, a common and effective method for researchers to discover relevant work. A large-scale user study of 122 participants from diverse backgrounds and experiences showed that Argo Scholar is effective at helping users find related work and understand paper connections, and incremental graph-based exploration is effective across diverse disciplines. Developed using modern web technologies, our tool is widely available at https://poloclub.github.io/argo-scholar and allows users to easily share their literature search results as a URL.
An Adaptive Framework for Confidence-constraint Rule Set Learning Algorithm in Large Dataset
Authors: Meng Li, Lu Yu, Ya-Lin Zhang, Xiaoguang Huang, Qitao Shi, Qing Cui, Xinxing Yang, Longfei Li, Wei Zhu, Yanming Fang, Jun Zhou
Venue: CIKM
Year: 2022

Decision rules have been successfully used in various classification applications because of their interpretability and efficiency. In many real-world scenarios, especially in industrial applications, it is necessary to generate rule sets under certain constraints, such as confidence constraints. However, most previous rule mining methods only emphasize the accuracy of the rule set but take no consideration of these constraints. In this paper, we propose a Confidence-constraint Rule Set Learning (CRSL) framework consisting of three main components, i.e. rule miner, rule ranker, and rule subset selector. Our method not only considers the trade-off between confidence and coverage of the rule set but also considers the trade-off between interpretability and performance. Experiments on benchmark data and large-scale industrial data demonstrate that the proposed method is able to achieve better performance (6.7% and 8.8% improvements) and competitive interpretability when compared with other rule set learning methods.
GCWSNet: Generalized Consistent Weighted Sampling for Scalable and Accurate Training of Neural Networks
Authors: Ping Li, Weijie Zhao
Venue: CIKM
Year: 2022

We propose using "powered generalized min-max'' (pGMM) hashed (linearized) via the "generalized consistent weighted sampling'' (GCWS) for training (deep) neural networks (hence the name "GCWSNet''). The pGMM and several related kernels were proposed in 2017. We demonstrate that pGMM hashed by GCWS provide a numerically stable scheme for applying power transformation on the original data, regardless of the magnitude of p and the data. Our experiments show that GCWSNet often improves the accuracy. It is also evident that GCWSNet converges substantially faster, reaching reasonable accuracy with merely one epoch of the training process. This property is much desired because many applications, such as advertisement click-through rate (CTR) prediction models, or data streams (i.e., data seen only once), often train just one epoch. Another beneficial side effect is that the computations of the first layer of the neural networks become additions instead of multiplications because the input data become binary and highly sparse. Empirical comparisons with (normalized) random Fourier features (NRFF) are provided. We also propose to reduce the model size of GCWSNet by count-sketch and develop the theory for analyzing the impact of using count-sketch on the accuracy of GCWS. Our analysis shows that an "8-bit'' strategy should provide the good trade-off between accuracy and model size. There are other ways to take advantage of GCWS. For example, one can apply GCWS on the last layer to boost the accuracy of trained deep neural nets.
SK2: Integrating Implicit Sentiment Knowledge and Explicit Syntax Knowledge for Aspect-Based Sentiment Analysis
Authors: Jia Li, Yuyuan Zhao, Zhi Jin, Ge Li, Tao Shen, Zhengwei Tao, Chongyang Tao
Venue: CIKM
Year: 2022

Aspect-based sentiment analysis (ABSA) plays an indispensable role in web mining and retrieval system as it involves a wide range of tasks, including aspect term extraction, opinion term extraction, aspect sentiment classification, etc. Early works are merely applicable to a part of these tasks, leading to computation-unfriendly models and a pipeline framework. Recently, a unified framework has been proposed to learn all these ABSA tasks in an end-to-end fashion. Despite its versatility, its performance is still sub-optimal since ABSA tasks depend heavily on both sentiment and syntax knowledge, but existing task-specific knowledge integration methods are hardly applicable to such a unified framework. Therefore, we propose a brand-new unified framework for ABSA in this work, which incorporates both implicit sentiment knowledge and explicit syntax knowledge to better complete all ABSA tasks. To effectively incorporate implicit sentiment knowledge, we first design a self-supervised pre-training procedure that is general enough to all ABSA tasks. It consists of conjunctive words prediction (CWP) task, sentiment-word polarity prediction (SPP) task, attribute nouns prediction (ANP) task, and sentiment-oriented masked language modeling (SMLM) task. Empowered by the pre-training procedure, our framework acquires strong abilities in sentiment representation and sentiment understanding. Meantime, considering a subtle syntax variation can significantly affect ABSA, we further explore a sparse relational graph attention network (SR-GAT) to introduce explicit aspect-oriented syntax knowledge. By combining both worlds of knowledge, our unified model can better represent and understand the input texts towards all ABSA tasks. Extensive experiments show that our proposed framework achieves consistent and significant improvements on all ABSA tasks.
Fifty Shades of Pink: Understanding Color in e-commerce using Knowledge Graphs
Authors: Lizzie Liang, Sneha Kamath, Petar Ristoski, Qunzhi Zhou, Zhe Wu
Venue: CIKM
Year: 2022

The color of the products is one of the most prevalent aspects in many e-commerce domains, and it is one of the decisive purchasing factors. Besides having thousands of color variations and shades, many brands continuously develop proprietary colors and color names to attract more customers. This often leads to color ambiguity (textual and visual), and vocabulary mismatch between buyers and sellers. Therefore, it is crucial for any e-commerce search engine to correctly identify the buyer's color intent and match it to the corresponding product listings. To address this challenge, in this work, we introduce a color query expansion approach using color Knowledge Graphs. We use Knowledge Graphs to unambiguously identify all the colors based on their properties, and the relationships to other colors, which allows us to perform semantic query expansion. Similar expansion concepts could be applied to domains outside of color.