OpenRaiser/Intern-Atlas huggingface dataset:
Spatio-temporal Trajectory Learning using Simulation Systems
Authors: Daniel Glake, Fabian Panse, Ulfia Lenfers, Thomas Clemen, Norbert Ritter
Venue: CIKM
Year: 2022

Spatio-temporal trajectories are essential factors for systems used in public transport, social ecology, and many other disciplines where movement is a relevant dynamic process. Each trajectory describes multiple state changes over time, induced by individual decision-making, based on psychological and social factors with physical constraints. Since a crucial factor of such systems is to reason about the potential trajectories in a closed environment, the primary problem is the realistic replication of individual decision making. Mental factors are often uncertain, not available or cannot be observed in reality. Thus, models for data generation must be derived from abstract studies using probabilities. To solve these problems, we present Multi-Agent-Trajectory-Learning (MATL), a state transition model to learn and generate human-like Spatio-temporal trajectory data. MATL combines Generative Adversarial Imitation Learning (GAIL) with a simulation system that uses constraints given by an agent-based model (Aℬℳ). We use GAIL to learn policies in conjunction with the Aℬℳ, resulting in a novel concept of individual decision making. Experiments with standard trajectory predictions show that our approach produces similar results to real-world observations.
Will This Online Shopping Session Succeed? Predicting Customer's Purchase Intention Using Embeddings
Authors: Miguel Alves Gomes, Richard Meyes, Philipp Meisen, Tobias Meisen
Venue: CIKM
Year: 2022

Customers are increasingly using online channels to buy products. For e-commerce companies, this offers new opportunities to tailor the shopping experience to customers' needs. Therefore, it is of great importance for a company to know their customers' intentions while browsing their webpage. A major challenge is the real-time analysis of a customer's intention during browsing sessions. To this end, a representation of the customer's browsing behavior must be retrieved from their live interactions on the webpage. Typically, characteristic behavioral features are extracted manually based on the knowledge of marketing experts. In this paper, we propose a customer embedding representation that is based on the customer's click-events recorded during browsing sessions. Thus, our approach does not use manually extracted features and is not based on marketing expert domain knowledge, which makes it transferable to different webpages and different online markets. We demonstrate our approach using three different e-commerce datasets to successfully predict whether a customer is going to purchase a specific product. For the prediction, we utilize the customer embedding representations as input for different machine learning models. We compare our approach with existing state-of-the-art approaches for real-time purchase prediction and show that our proposed customer representation with an LSTM predictor outperforms the state-of-the-art approach on all three datasets. Additionally, the creation process of our customers' representation is on average 235 times faster than the creation process of the baseline.
ITSM-GCN: Informative Training Sample Mining for Graph Convolutional Network-based Collaborative Filtering
Authors: Kaiqi Gong, Xiao Song, Senzhang Wang, Songsong Liu, Yong Li
Venue: CIKM
Year: 2022

Recently, graph convolutional network (GCN) has become one of the most popular and state-of-the-art collaborative filtering (CF) methods. Existing GCN-based CF studies have made many meaningful and excellent efforts at loss function design and embedding propagation improvement. Despite their successes, we argue that existing methods have not yet properly explored more effective sampling strategy, including both positive sampling and negative sampling. To tackle this limitation, a novel framework named ITSM-GCN is proposed to carry out our designed Informative Training Sample Mining (ITSM) sampling strategy for the learning of GCN-based CF models. Specifically, we first adopt and improve the dynamic negative sampling (DNS) strategy, which achieves considerable improvements in both training efficiency and recommendation performance. More importantly, we design two potentially positive training sample mining strategies, namely a similarity-based sampler and score-based sampler, to further enhance GCN-based CF. Extensive experiments show that ITSM-GCN significantly outperforms state-of-the-art GCN-based CF models, including LightGCN, SGL-ED and SimpleX. For example, ITSM-GCN improves on SimpleX by 12.0%, 3.0%, and 1.2% on Recall@20 for Amazon-Books, Yelp2018 and Gowalla, respectively.
Leveraging Scalable Profiling to Learn and Visualize the Latest Trustworthy COVID-19 Medical Research Findings
Authors: Michael N. Gubanov, Sophie Pavia, Anna Pyayt, William Goble
Venue: CIKM
Year: 2022

Accessing large-scale structured datasets such as WDC [31] or CORD-191 is very challenging [ 11, 13, 14, 41, 42]. Even if one topic (e.g. Vaccine Side-Effects) is of interest, the side-effects tables in different papers have hundreds of different schemas, depending on the authors, which significantly complicates both finding and querying them. Here we demonstrate our scalable Meta-data profiler, capable of constructing a standardized interface to a topic of interest in large-scale structured datasets. This interface, called Meta-profile represents a meta-data summary per each topic, representative of the entire dataset. Such profiles can be used as a robust visualization as well as to simplify access to structured data for both data scientists and end users at scale [32, 42]
Binary Transformation Method for Multi-Label Stream Classification
Authors: Ege Berkay Gulcan, Isin Su Ecevit, Fazli Can
Venue: CIKM
Year: 2022

Data streams produce extensive data with high throughput from various domains and require copious amounts of computational resources and energy. Many data streams are generated as multi-labeled and classifying this data is computationally demanding. Some of the most well-known methods for Multi-Label Stream Classification are Problem Transformation schemes; however, previous work on this area does not satisfy the efficiency demands of multi-label data streams. In this study, we propose a novel Problem Transformation method for Multi-Label Stream Classification called Binary Transformation, which utilizes regression algorithms by transforming the labels into a continuous value. We compare our method against three of the leading problem transformation methods using eight datasets. Our results show that Binary Transformation achieves statistically similar effectiveness and provides a much higher level of efficiency.
Calibrated Conversion Rate Prediction via Knowledge Distillation under Delayed Feedback in Online Advertising
Authors: Yuyao Guo, Haoming Li, Xiang Ao, Min Lu, Dapeng Liu, Lei Xiao, Jie Jiang, Qing He
Venue: CIKM
Year: 2022

Prevailing calibration methods may fail to generalize well due to the pervasively delayed feedback issue in online advertising. That is, the labels of recent samples are more likely to be inaccurate because of the delayed feedback by users, while the old samples with complete feedback may suffer from the data shift compared to the recent ones. In this paper, we propose to calibrate conversion rate prediction models considering delayed feedback via the knowledge distillation technique. Specifically, we deploy a teacher model modeling by the samples with complete feedback to learn long-term conversion patterns and a student model modeling by the recent data to reduce the impact of data shift. We also devise a distillation loss to buoy the student model to learn from the teacher. Experimental results on two real-world advertising conversion rate prediction datasets demonstrate that our method can provide more calibrated predictions compared with the existing ones. We also exhibit that our method can be extended to different base models.
UDM: A Unified Deep Matching Framework in Recommender Systems
Authors: Long Guo, Fei Fang, Binqiang Zhao, Bin Cui
Venue: CIKM
Year: 2022

Due to the large-scale users and items, industrial recommender systems usually consist of two stages, the matching stage and the ranking stage. The matching stage is responsible for retrieving a small fraction of relevant items from the large-scale item pool which are further selected by the ranking stage. Most of the existing deep learning-based matching models focus on the problem of modeling user interest representation by using inner product between user representation and item representation to obtain the user-to-item relevance. However, the item-to-item relevance between user interacted item and target item is not considered in the deep matching models which is computationally prohibitive for large-scale applications. In this paper, we propose a unified deep matching framework called UDM for the matching stage to mitigate this issue. UDM can model the user-to-item relevance and item-to-item relevance simultaneously with the help of an interest extraction module and interest interaction module, respectively. Specifically, the interest extraction module is used as the main network to extract users' multiple interests with multiple vectors based on users' behavior sequences, while the interest interaction module is used as an auxiliary network to supervise the learning of the interest extraction module, which can model the interaction between user interacted items and target item. In the experiments conducted on two public datasets and a large-scale industrial dataset, UDM achieves consistent improvements over state-of-the-art models. Moreover, UDM has been deployed in the operational system of Alibaba. Online A/B testing results further reveal the effectiveness of UDM. To the best of our knowledge, UDM is the first deep matching framework which combines the user-to-item relevance modeling and item-to-item relevance modeling in the same model.
KiCi: A Knowledge Importance Based Class Incremental Learning Method for Wearable Activity Recognition
Authors: Shuai Guo, Yang Gu, Shijie Wen, Yuan Ma, Yiqiang Chen, Jiwei Wang, Chunyu Hu
Venue: CIKM
Year: 2022

Wearable-based human activity recognition (HAR) is commonly employed in real-world scenarios such as health monitoring, auxiliary diagnosis, etc. As implementing activity recognition is a daunting challenge in an open dynamic environment, incremental learning has become a common method to adapt to variable behavior patterns of users and create dynamic modeling in activity recognition. However, catastrophic forgetting is a significant challenge with incremental learning. This is contrary to our expectations of identifying new activity classes while remembering existing ones. To address this problem, we propose a knowledge importance-based class incremental learning method called KiCi and construct an incremental learning model based on the framework of self-iterative knowledge distillation for dynamic activity recognition. To eliminate the prediction bias of the teacher model on the old knowledge, we utilize the trained weights of previous incremental steps generated by the teacher model as the prior knowledge to obtain knowledge importance. Then use it to make the student model have a reasonable trade-off between old and new knowledge and mitigate catastrophic forgetting by avoiding negative transfer. We conduct extensive experiments on four public HAR datasets and our method consistently outperforms the existing state-of-the-art methods by a large margin.
Fusing Geometric and Scene Information for Cross-View Geo-Localization
Authors: Siyuan Guo, Tianying Liu, Wengen Li, Jihong Guan, Shuigeng Zhou
Venue: CIKM
Year: 2022

Cross-view geo-localization is to match scene images (e.g. ground-view images) with geo-tagged aerial images, which is crucial to a wide range of applications such as autonomous driving and street view navigation. Existing methods can neither address the perspective difference well nor effectively capture the scene information. In this work, we propose a Geometric and Scene Information Fusion (GSIF) model for more accurate cross-view geo-localization. GSIF first learns the geometric information of scene images and aerial images via log-polar transformation and spatial-attention aggregation to alleviate the perspective difference. Then, it mines the scene information of scene images via Sky View Factor (SVF) extraction. Finally, both geometric information and scene information are fused for image matching, and a balanced loss function is introduced to boost the matching accuracy. Experimental results on two real datasets show that our model can significantly outperforms the existing methods.
SpCQL: A Semantic Parsing Dataset for Converting Natural Language into Cypher
Authors: Aibo Guo, Xinyi Li, Guanchen Xiao, Zhen Tan, Xiang Zhao
Venue: CIKM
Year: 2022

The Neo4j query language Cypher enables efficient querying for graphs and has become the most popular graph database language. Due to its complexities, semantic parsing (similar to Text-to-SQL) that translates natural language queries to Cypher becomes highly desirable. We propose the first Text-to-CQL dataset, SpCQL, which contains one Neo4j graph database, 10,000 manually annotated natural language queries and the matching Cypher queries (CQL). Correspondingly, based on this dataset, we define a new semantic parsing task Text-to-CQL. The Text-to-CQL task differs from the traditional Text-to-SQL task due to CQL being more flexible and versatile, especially for schema queries, which brings precedented challenges for the translation process. Although current SOTA Text-to-SQL models utilize SQL schema and contents, they do not scale up to large-scale graph databases. Besides, due to the absence of the primary and foreign keys in Cypher, which are essential for the multi-table Text-to-SQL task, existing Text-to-SQL models are rendered ineffective in this new task and have to be adapted to work. We propose three baselines based on the Seq2Seq framework and conduct experiments on the SpCQL dataset. The experiments yield undesirable results for existing models, hence pressing for subsequent research that considers the characteristics of SQL. The dataset is available at https://github.com/Guoaibo/Text-to-CQL.
Bootstrap-based Causal Structure Learning
Authors: Xianjie Guo, Yujie Wang, Xiaoling Huang, Shuai Yang, Kui Yu
Venue: CIKM
Year: 2022

Learning a causal structure from observational data is crucial for data scientists. Recent advances in causal structure learning (CSL) have focused on local-to-global learning, since the local-to-global CSL can be scaled to high-dimensional data. The local-to-global CSL algorithms first learn the local skeletons, then construct the global skeleton, and finally orient edges. In practice, the performance of local-to-global CSL mainly depends on the accuracy of the global skeleton. However, in many real-world settings, owing to inevitable data quality issues (e.g. noise and small sample), existing local-to-global CSL methods often yield many asymmetric edges (e.g., given anasymmetric edge containing variables A and B, the learned skeleton of A contains B, but the learned skeleton of B does not contain A), which make it difficult to construct a high quality global skeleton. To tackle this problem, this paper proposes a Bootstrap sampling based Causal Structure Learning (BCSL) algorithm. The novel contribution of BCSL is that it proposes an integrated global skeleton learning strategy that can construct more accurate global skeletons. Specifically, this strategy first utilizes the Bootstrap method to generate multiple sub-datasets, then learns the local skeleton of variables on each asymmetric edge on those sub-datasets, and finally designs a novel scoring function to estimate the learning results on all sub-datasets for correcting the asymmetric edge. Extensive experiments on both benchmark and real datasets verify the effectiveness of the proposed method.
Learning Hypersphere for Few-shot Anomaly Detection on Attributed Networks
Authors: Qiuyu Guo, Xiang Zhao, Yang Fang, Shiyu Yang, Xuemin Lin, Dian Ouyang
Venue: CIKM
Year: 2022

The existence of anomalies is quite common, but they are hidden within the complex structure and high-dimensional node attributes of the attributed networks. As a latent hazard in existing systems, anomalies can be transformed into important instruction information once we detect them, e.g., computer network admins can react to the leakage of sensitive data if network traffic anomalies are identified. Extensive research in anomaly detection on attributed networks has proposed various techniques, which do improve the quality of data in networks, while they rarely cope with the few-shot anomaly detection problem. Few-shot anomaly detection task with only a few dozen labeled anomalies is more practical since anomalies are rare in number for real-world systems. We propose a few-shot anomaly detection approach for detecting the anomaly nodes that significantly deviate from the vast majority. Our approach, based on an extension of model-agnostic meta-learning(MAML), is a Learnable Hypersphere Meta-Learning method running on local subgraphs named LHML. LHML learns on a single subgraph, conducts meta-learning on a set of subgraphs, and maintains the radius of a learnable hypersphere across subgraphs to detect anomalies efficiently. The learnable hypersphere is a changing boundary that can be used to identify anomalies in an unbalanced binary-classification setting and quickly adapt to a new subgraph by a few gradient updating steps of MAML. Furthermore, our model runs across subgraphs, making it possible to identify an anomaly without requiring the whole graph nodes as is usually the way but only a handful of nodes around it, which means LHML can scale to large networks. Experimental results show the effective performance of LHML on benchmark datasets.
Sentaur: Sensor Observable Data Model for Smart Spaces
Authors: Peeyush Gupta, Sharad Mehrotra, Shantanu Sharma, Roberto Yus, Nalini Venkatasubramanian
Venue: CIKM
Year: 2022

This paper presents Sentaur, a middleware designed, built, and deployed to support sensor-based smart space analytical applications. Sentaur supports a powerful data model that decouples semantic data (about the application domain) from sensor data (using which the semantic data is derived). By supporting mechanisms to map/translate data, concepts, and queries between the two levels, Sentaur relieves application developers from having to know or reason about either capabilities of sensors or write sensor specific code. This paper describes Sentaur's data model, its translation strategy, and highlights its benefits through real-world case studies.
Multi-Aggregator Time-Warping Heterogeneous Graph Neural Network for Personalized Micro-Video Recommendation
Authors: Jinkun Han, Wei Li, Zhipeng Cai, Yingshu Li
Venue: CIKM
Year: 2022

Micro-video recommendation is attracting global attention and becoming a popular daily service for people of all ages. Recently, Graph Neural Networks-based micro-video recommendation has displayed performance improvement for many kinds of recommendation tasks. However, the existing works fail to fully consider the characteristics of micro-videos, such as the high timeliness of news nature micro-video recommendation and sequential interactions of frequently changed interests. In this paper, a novel Multi-aggregator Time-warping Heterogeneous Graph Neural Network (MTHGNN) is proposed for personalized news nature micro-video recommendation based on sequential sessions, where characteristics of micro-videos are comprehensively studied, users' preference is mined via multi-aggregator, the temporal and dynamic changes of users' preference are captured, and timeliness is considered. Through the comparison with the state-of-the-arts, the experimental results validate the superiority of our MTHGNN model.
Long-tail Mixup for Extreme Multi-label Classification
Authors: Sangwoo Han, Eunseong Choi, Chan Lim, Hyunjung Shim, Jongwuk Lee
Venue: CIKM
Year: 2022

Extreme multi-label classification (XMC) aims at finding multiple relevant labels for a given sample from a huge label set at the industrial scale. The XMC problem inherently poses two challenges: scalability and label sparsity - the number of labels is too large, and labels follow the long-tail distribution. To resolve these problems, we propose a novel Mixup-based augmentation method for long-tail labels, called TailMix. Building upon the partition-based model, TailMix utilizes the context vectors generated from the label attention layer. It first selectively chooses two context vectors using the inverse propensity score of labels and the label proximity graph representing the co-occurrence of labels. Using two context vectors, it augments new samples with the long-tail label to improve the accuracy of long-tail labels. Despite its simplicity, experimental results show that TailMix consistently outperforms other augmentation methods on three benchmark datasets, especially for long-tail labels in terms of two metrics, PSP@k and PSN@k.
MLadder: An Online Training System for Machine Learning and Data Science Education
Authors: Siqi Han, Wanting Li, En Zhang, Jilin Shi, Wei Wang, Xuesong Lu
Venue: CIKM
Year: 2022

Education on machine learning and data science has drawn a lot of attention in both higher education and vocational training. Although various tools and services such as Jupyter Notebook and Google Cloud's AI have been developed for building and training models, they are not suitable for direct use in educational settings. For example, teachers expect a platform where they can easily distribute and grade programming assignments, and students want to quickly start coding and training models without the burden of setting up an environment. To this end, we develop MLadder, an online training system for machine learning and data science education. Specifically, we seamlessly integrate two open-source software, CodaLab and Jupyter Notebook, which are used for hosting assignments and building models, respectively. Moreover, we devise several methods to make the system lightweight and scalable, so that it can be deployed on-premises even with limited resources. We have used MLadder in the machine learning and data science courses in our school and facilitated both teaching and learning.
OpenHGNN: An Open Source Toolkit for Heterogeneous Graph Neural Network
Authors: Hui Han, Tianyu Zhao, Cheng Yang, Hongyi Zhang, Yaoqi Liu, Xiao Wang, Chuan Shi
Venue: CIKM
Year: 2022

Heterogeneous Graph Neural Networks (HGNNs), as a kind of powerful graph representation learning methods on heterogeneous graphs, have attracted increasing attention of many researchers. Although, several existing libraries have supported HGNNs, they just provide the most basic models and operators. Building and benchmarking various downstream tasks on HGNNs is still painful and time consuming with them. In this paper, we will introduce OpenHGNN, an open-source toolkit for HGNNs. OpenHGNN defines a unified and standard pipeline for training and testing, which can allow users to run a model on a specific dataset with just one command line. OpenHGNN has integrated 20+ mainstream HGNNs and 20+ heterogeneous graph datasets, which can be used for various advanced tasks, such as node classification, link prediction, and recommendation. In addition, thanks to the modularized design of OpenHGNN, it can be extended to meet users' customized needs. We also release several novel and useful tools and features, including leaderboard, autoML, design space, and visualization, to provide users with better usage experiences. OpenHGNN is an open-source project, and the source code is available at https://github.com/BUPT-GAMMA/OpenHGNN.