OpenRaiser/Intern-Atlas huggingface dataset:
Minimizing Polarization in Noisy Leader-Follower Opinion Dynamics
Authors: Wanyue Xu, Zhongzhi Zhang
Venue: CIKM
Year: 2023

The operation of creating edges has been widely applied to optimize relevant quantities of opinion dynamics. In this paper, we consider a problem of polarization optimization for the leader-follower opinion dynamics in a noisy social network with n nodes and m edges, where a group Q of q nodes are leaders, and the remaining n-q nodes are followers. We adopt the popular leader-follower DeGroot model, where the opinion of every leader is identical and remains unchanged, while the opinion of every follower is subject to white noise. The polarization is defined as the steady-state variance of the deviation of each node's opinion from leaders' opinion, which equals one half of the effective resistance RQ between the node group Q and all other nodes. Concretely, we propose and study the problem of minimizing RQ by adding k new edges with each incident to a node in Q. We show that the objective function is monotone and supermodular. We then propose a simple greedy algorithm with an approximation factor 1-1/e that approximately solves the problem in O((n-q)3) time. To speed up the computation, we also provide a fast algorithm to compute (1-1/e-ε)-approximate effective resistance RQ, the running time of which is O~ (mkε-2) for any ε>0, where the O~(·) notation suppresses the poly(log n) factors. Extensive experiment results show that our second algorithm is both effective and efficient.
Time-series Shapelets with Learnable Lengths
Authors: Akihiro Yamaguchi, Ken Ueno, Hisashi Kashima
Venue: CIKM
Year: 2023

Shapelets are subsequences that are effective for classifying time-series instances. Learning shapelets by a continuous optimization has recently been studied to improve computational efficiency and classification performance. However, existing methods have employed predefined and fixed shapelet lengths during the continuous optimization, despite the fact that shapelets and their lengths are inherently interdependent and thus should be jointly optimized. To efficiently explore shapelets of high quality in terms of interpretability and inter-class separability, this study makes the shapelet lengths continuous and learnable. The proposed formulation jointly optimizes not only a binary classifier and shapelets but also shapelet lengths. The derived SGD optimization can be theoretically interpreted as improving the quality of shapelets in terms of shapelet closeness to the time series for target / off-target classes. We demonstrate improvements in area under the curve, total training time, and shapelet interpretability on UCR binary datasets.
Logistics Audience Expansion via Temporal Knowledge Graph
Authors: Hua Yan, Yingqiang Ge, Haotian Wang, Desheng Zhang, Yu Yang
Venue: CIKM
Year: 2023

Logistics audience expansion, the process for logistics companies to find potential long-term customers, is one of the most important tasks for business growth. However, existing methods for conventional audience expansion fall short due to two significant challenges, the intricate interplay of multiple complex factors in the logistics scenario and the emphasis on long-term logistics service usage instead of one-time promotions. To address the above limitations, we design LOGAE-TKG, a logistics audience expansion method based on a temporal knowledge graph, which consists of three components: (i) a temporal logistics knowledge graph pre-trained model to model the effect of multiple complex factors and build a solid logistics knowledge base for contracting and usage prediction; (ii) an intention learning model with data augmentation-based comparison to capture the contracting intention; (iii) a future pattern discovery model to uncover post-contract patterns. We evaluate and deploy our method on the JingDong e-commerce platform. Extensive offline experiment results and real-world deployment results demonstrate the effectiveness of our method.
Few-Shot Learning via Task-Aware Discriminant Local Descriptors Network
Authors: Leilei Yan, Fanzhang Li, Xiaohan Zheng, Li Zhang
Venue: CIKM
Year: 2023

Few-shot learning for image classification task aims to classify images from several novel classes with limited number of samples. Recent studies have shown that the deep local descriptors have better representation ability than image-level features, and achieve great success. However, most of these methods often use all local descriptors or over-screening local descriptors for classification. The former contains some task-irrelevant descriptors, which may misguide the final classification result. The latter is likely to lose some key descriptors. In this paper, we propose a novel Task-Aware Discriminant local descriptors Network (TADNet) to address these issues, which can adaptively select the discriminative query descriptors and eliminate the task-irrelevant query descriptors among the entire task. Specifically, TADNet assigns a value to each query descriptor by comparing its similarity to all support classes to represent its discriminant power for classification. Then the discriminative query descriptors can be preserved via a task-aware attention map. Extensive experiments on both fine-grained and generalized datasets demonstrate that the proposed TADNet outperforms the existing state-of-the-art methods.
Identifying Regional Driving Risks via Transductive Cross-City Transfer Learning Under Negative Transfer
Authors: Hua Yan, Hao Wang, Desheng Zhang, Yu Yang
Venue: CIKM
Year: 2023

Identifying regional driving risks is important for real-world applications such as driving safety warning applications, public safety management, and insurance company premium pricing. Previous approaches are either based on traffic accident reports or vehicular sensor data. They either fail to identify potential risks, such as near-miss collisions, which would need other important measurements (e.g., hard break, acceleration, etc.), or fail to generalize to cities without vehicular sensor data, severely limiting their practicality. In this work, we address these two challenges and successfully identify regional driving risks in a target city without vehicular sensor data via cross-city transfer learning. Specifically, we design a novel framework RiskTrans by optimizing both the predictor and the relationship between cities to achieve transfer learning. We advance the existing works from two aspects: (i) we achieve it in a transductive manner without accessing labeled data in the target cities; (ii) we identify and address the problem of negative transfer in cross-city transfer learning, a prominent issue that is often (surprisingly) neglected in previous works. Finally, we conduct extensive experiments based on data collected from 175 thousand vehicles in six cities. The results show RiskTrans outperforms baselines by at least 50.2% and reduces negative transfer by 49.4%.
Mitigating Semantic Confusion from Hostile Neighborhood for Graph Active Learning
Authors: Tianmeng Yang, Min Zhou, Yujing Wang, Zhengjie Lin, Lujia Pan, Bin Cui, Yunhai Tong
Venue: CIKM
Year: 2023

Graph Active Learning (GAL), which aims to find the most informative nodes in graphs for annotation to maximize the Graph Neural Networks (GNNs) performance, has attracted many research efforts but remains non-trivial challenges. One major challenge is that existing GAL strategies may introduce semantic confusion to the selected training set, particularly when graphs are noisy. Specifically, most existing methods assume all aggregating features to be helpful, ignoring the semantically negative effect between inter-class edges under the message-passing mechanism. In this work, we present Semantic-aware Active learning framework for Graphs (SAG) to mitigate the semantic confusion problem. Pairwise similarities and dissimilarities of nodes with semantic features are introduced to jointly evaluate the node influence. A new prototype-based criterion and query policy are also designed to maintain diversity and class balance of the selected nodes, respectively. Extensive experiments on the public benchmark graphs and a real-world financial dataset demonstrate that SAG significantly improves node classification performances and consistently outperforms previous methods. Moreover, comprehensive analysis and ablation study also verify the effectiveness of the proposed framework.
MC-DRE: Multi-Aspect Cross Integration for Drug Event/Entity Extraction
Authors: Jie Yang, Soyeon Caren Han, Siqu Long, Josiah Poon, Goran Nenadic
Venue: CIKM
Year: 2023

Extracting meaningful drug-related information chunks, such as adverse drug events (ADE), is crucial for preventing morbidity and saving many lives. Most ADEs are reported via an unstructured conversation with the medical context, so applying a general entity recognition approach is not sufficient enough. In this paper, we propose a new multi-aspect cross-integration framework for drug entity/event detection by capturing and aligning different context/language/knowledge properties from drug-related documents. We first construct multi-aspect encoders to describe semantic, syntactic, and medical document contextual information by conducting those slot tagging tasks, main drug entity/event detection, part-of-speech tagging, and general medical named entity recognition. Then, each encoder conducts cross-integration with other contextual information in three ways: the key-value cross, attention cross, and feedforward cross, so the multi-encoders are integrated in depth. Our model outperforms all SOTA on two widely used tasks, flat entity detection and discontinuous event extraction.
Mulco: Recognizing Chinese Nested Named Entities through Multiple Scopes
Authors: Jiuding Yang, Jinwen Luo, Weidong Guo, Jerry Chen, Di Niu, Yu Xu
Venue: CIKM
Year: 2023

Nested Named Entity Recognition (NNER), as a subarea of Named Entity Recognition, has presented longstanding challenges to researchers. In NNER, one entity may be part of a larger entity, which can occur at multiple levels. These nested structures prevent traditional sequence labeling methods from properly recognizing all entities. While recent research has focused on designing better recognition methods for NNER in various languages, Chinese Nested Named Entity Recognition (CNNER) is still underdeveloped, largely due to a lack of freely available CNNER benchmarks. To support CNNER research, in this paper, we introduce ChiNesE, a CNNER dataset comprising 20,000 sentences from online passages in multiple domains and containing 117,284 entities that fall into 10 categories, of which 43.8% are nested named entities. Based on ChiNesE, we propose Mulco, a novel method that can recognize named entities in nested structures through multiple scopes. Each scope uses a scope-based sequence labeling method that predicts an anchor and the length of a named entity to recognize it. Experimental results show that Mulco outperforms state-of-the-art baseline methods with different recognition schemes on ChiNesE and ACE 2005 Chinese corpus.
Group Identification via Transitional Hypergraph Convolution with Cross-view Self-supervised Learning
Authors: Mingdai Yang, Zhiwei Liu, Liangwei Yang, Xiaolong Liu, Chen Wang, Hao Peng, Philip S. Yu
Venue: CIKM
Year: 2023

With the proliferation of social media, a growing number of users search for and join group activities in their daily life. This develops a need for the study on the group identification (GI) task, i.e., recommending groups to users. The major challenge in this task is how to predict users' preferences for groups based on not only previous group participation of users but also users' interests in items. Although recent developments in Graph Neural Networks (GNNs) accomplish embedding multiple types of objects in graph-based recommender systems, they, however, fail to address this GI problem comprehensively. In this paper, we propose a novel framework named Group Identification via Transitional Hypergraph Convolution with Graph Self-supervised Learning (GTGS). We devise a novel transitional hypergraph convolution layer to leverage users' preferences for items as prior knowledge when seeking their group preferences. To construct comprehensive user/group representations for GI task, we design the cross-view self-supervised learning to encourage the intrinsic consistency between item and group preferences for each user, and the group-based regularization to enhance the distinction among group embeddings. Experimental results on three benchmark datasets verify the superiority of GTGS. Additional detailed investigations are conducted to demonstrate the effectiveness of the proposed framework.
Co-guided Random Walk for Polarized Communities Search
Authors: Fanyi Yang, Huifang Ma, Cairui Yan, Zhixin Li, Liang Chang
Venue: CIKM
Year: 2023

Polarized Communities Search (PCS) aims to identify query-dependent communities where positive links predominantly connect nodes within each community, while negative links primarily connect nodes across different communities. Existing solutions primarily focus on modeling network topology, disregarding the crucial factor of node attributes. However, it is non-trivial to incorporate node attributes into PCS. In this paper, we propose a novel method called CO-guided RAndom walk in attributed signed networks (CORA) for PCS. Our approach involves constructing an attribute-based signed network to represent the auxiliary relations between nodes. We introduce a weight assignment mechanism to assess the reliability of edges in the signed network. Then, we design a co-guided random walk scheme that operates on two signed networks to model the connections between network topology and node attributes, thereby enhancing the search outcomes. Finally, we identify polarized communities using the Rayleigh quotient in the signed network. Extensive experiments conducted on three public datasets demonstrate the superior performance of CORA compared to state-of-the-art baselines for polarized communities search.
Graph Exploration Matters: Improving both Individual-Level and System-Level Diversity in WeChat Feed Recommendation
Authors: Shuai Yang, Lixin Zhang, Feng Xia, Leyu Lin
Venue: CIKM
Year: 2023

There are roughly three stages in real industrial recommendation systems, candidates generation (retrieval), ranking and reranking. Both individual-level diversity and system-level diversity are important in this framework. The former focus on each single user's experience, while the latter focus on the difference among users. Graph-based retrieval strategies are inevitably hijacked by heavy users and popular items, leading to similar results for each user and the convergence among users. In the reranking phase, Determinantal Point Process (DPP) is widely deployed to increase individual-level diversity. Heavily relying on the semantic information of items, DPP suffers from clickbait and inaccurate categories. Besides, most studies only focus on one of the two levels of diversity, and ignore the mutual influence among different stages in real recommender systems. We argue that individual-level diversity and system-level diversity should be viewed as an integrated problem, and we provide an efficient and deployable solution for web-scale recommenders. Generally, we propose to employ the retrieval graph information in diversity-based reranking, by which to weaken the hidden similarity of items exposed to users, and consequently gain more graph explorations to improve the system-level diversity. Besides, we argue that users' propensity for diversity changes over time in content feed recommendation. Therefore, with the explored graph, we also propose to capture the user's real-time personalized propensity to the diversity. We implement and deploy the combined system in WeChat App's Top Stories used by hundreds of millions of users. Comparative and long-term online A/B tests show our solution can effectively improve both user engagement and system revenue.
Positive-Unlabeled Node Classification with Structure-aware Graph Learning
Authors: Hansi Yang, Yongqi Zhang, Quanming Yao, James T. Kwok
Venue: CIKM
Year: 2023

Node classification on graphs is an important research problem with many applications. Real-world graph data sets may not be balanced and accurate as assumed by most existing works. A challenging setting is positive-unlabeled (PU) node classification, where labeled nodes are restricted to positive nodes. It has diverse applications, e.g., pandemic prediction or network anomaly detection. Existing works on PU node classification overlook information in the graph structure, which can be critical. In this paper, we propose to better utilize graph structure for PU node classification. We first propose a distance-aware PU loss that uses homophily in graphs to introduce more accurate supervision. We also propose a regularizer to align the model with graph structure. Theoretical analysis shows that minimizing the proposed loss also leads to minimizing the expected loss with both positive and negative labels. Extensive empirical evaluation on diverse graph data sets demonstrates its superior performance over existing state-of-the-art methods.
Explore Epistemic Uncertainty in Domain Adaptive Semantic Segmentation
Authors: Kai Yao, Zixian Su, Xi Yang, Jie Sun, Kaizhu Huang
Venue: CIKM
Year: 2023

In domain adaptive segmentation, domain shift may cause erroneous high-confidence predictions on the target domain, resulting in poor self-training. To alleviate the potential error, most previous works mainly consider aleatoric uncertainty arising from the inherit data noise. This may however lead to overconfidence in incorrect predictions and thus limit the performance. In this paper, we take advantage of Deterministic Uncertainty Methods (DUM) to explore the epistemic uncertainty, which reflects accurately the domain gap depending on the model choice and parameter fitting trained on source domain. The epistemic uncertainty on target domain is evaluated on-the-fly to facilitate online reweighting and correction in the self-training process. Meanwhile, to tackle the class-wise quantity and learning difficulty imbalance problem, we introduce a novel data resampling strategy to promote simultaneous convergence across different categories. This strategy prevents the class-level over-fitting in source domain and further boosts the adaptation performance by better quantifying the uncertainty in target domain. We illustrate the superiority of our method compared with the state-of-the-art methods.
NP-SSL: A Modular and Extensible Self-supervised Learning Library with Neural Processes
Authors: Zesheng Ye, Jing Du, Yao Liu, Yihong Zhang, Lina Yao
Venue: CIKM
Year: 2023

Neural Processes (NPs) are a family of supervised density estimators devoted to probabilistic function approximation with meta-learning. Despite extensive research on the subject, the absence of a unified framework for NPs leads to varied architectural solutions across diverse studies. This non-consensus poses challenges to reproducing and benchmarking different NPs. Moreover, existing codebases mainly prioritize generative density estimation, yet rarely consider expanding the capability of NPs to self-supervised representation learning, which however has gained growing importance in data mining applications. To this end, we present NP-SSL, a modular and configurable framework with built-in support, requiring minimal effort to 1) implement classical NPs architectures; 2) customize specific components; 3) integrate hybrid training scheme (e.g., contrastive); and 4) extend NPs to act as a self-supervised learning toolkit, producing latent representations of data, and facilitating diverse downstream predictive tasks. To illustrate, we discuss a case study that applies NP-SSL to model time-series data. We interpret that NP-SSL can handle different predictive tasks such as imputation and forecasting, by a simple switch in data samplings, without significant change to the underlying structure. We hope this study can reduce the workload of future research on leveraging NPs to tackle more a broader range of real-world data mining applications. Code and documentation are at https://github.com/zyecs/NP-SSL.
LAMM: Language Aware Active Learning for Multilingual Models
Authors: Ze Ye, Dantong Liu, Kaushik Pavani, Sunny Dasgupta
Venue: CIKM
Year: 2023

In industrial settings, it is often necessary to achieve language-level accuracy targets. For example, Amazon business teams need to build multilingual product classifiers that operate accurately in all European languages. It is unacceptable for the accuracy of product classification to meet the target in one language (e.g, English), while falling below the target in other languages (e.g, Portuguese). To fix such issues, we propose Language Aware Active Learning for Multilingual Models (LAMM), an active learning strategy that enables a classifier to learn from a small amount of labeled data in a targeted manner to improve the accuracy of Low-resource languages (LRLs) with limited amounts of data for model training. Our empirical results on two open-source datasets and two proprietary product classification datasets demonstrate that LAMM is able to improve the LRL performance by 4%--11% when compared to strong baselines.
Improving Query Correction Using Pre-train Language Model In Search Engines
Authors: Dezhi Ye, Bowen Tian, Jiabin Fan, Jie Liu, Tianhua Zhou, Xiang Chen, Mingming Li, Jin Ma
Venue: CIKM
Year: 2023

Query correction is a task that automatically detects and corrects errors in what users type into a search engine. Misspelled queries can lead to user dissatisfaction and churn. However, correcting a user query accurately is not an easy task. One major challenge is that a correction model must be capable of high-level language comprehension. Recently, pre-trained language models (PLMs) have been successfully applied to text correction tasks, but few works have been done on query correction. However, it is nontrivial to directly apply these PLMs to query correction in large-scale search systems due to the following challenging issues: 1) Expensive deployment. Deploying such a model requires expensive computations. 2) Lacking domain knowledge. A neural correction model needs massive training data to activate its power. To this end, we introduce KSTEM, a Knowledge-based Sequence To Edit Model for Chinese query correction. KSTEM transforms the sequence generation task into sequence tagging by mapping errors into five categories: KEEP, REPLACE, SWAP, DELETE, and INSERT, reducing computational complexity. Additionally, KSTEM adopts 2D position encoding, which is composed of the internal and external order of the words. Meanwhile, to compensate for the lack of domain knowledge, we propose a task-specific training paradigm for query correction, including edit strategy-based pre-training, user click-based post pre-train, and human label-based fine-tuning. Finally, we apply KSTEM to the industrial search system. Extensive offline and online experiments show that KSTEM significantly improves query correction performance. We hope that our experience will benefit frontier researchers.