OpenRaiser/Intern-Atlas huggingface dataset:
Cross-Network Social User Embedding with Hybrid Differential Privacy Guarantees
Authors: Jiaqian Ren, Lei Jiang, Hao Peng, Lingjuan Lyu, Zhiwei Liu, Chaochao Chen, Jia Wu, Xu Bai, Philip S. Yu
Venue: CIKM
Year: 2022

Integrating multiple online social networks (OSNs) has important implications for many downstream social mining tasks, such as user preference modelling, recommendation, and link prediction. However, it is unfortunately accompanied by growing privacy concerns about leaking sensitive user information. How to fully utilize the data from different online social networks while preserving user privacy remains largely unsolved. To this end, we propose a Cross-network Social User Embedding framework, namely DP-CroSUE, to learn the comprehensive representations of users in a privacy-preserving way. We jointly consider information from partially aligned social networks with differential privacy guarantees. In particular, for each heterogeneous social network, we first introduce a hybrid differential privacy notion to capture the variation of privacy expectations for heterogeneous data types. Next, to find user linkages across social networks, we make unsupervised user embedding-based alignment in which the user embeddings are achieved by the heterogeneous network embedding technology. To further enhance user embeddings, a novel cross-network GCN embedding model is designed to transfer knowledge across networks through those aligned users. Extensive experiments on three real-world datasets demonstrate that our approach makes a significant improvement on user interest prediction tasks as well as defending user attribute inference attacks from embedding.
RSD: A Reinforced Siamese Network with Domain Knowledge for Early Diagnosis
Authors: Houxing Ren, Jingyuan Wang, Wayne Xin Zhao
Venue: CIKM
Year: 2022

The availability of electronic health record data makes it possible to develop automatic disease diagnosis approaches. In this paper, we study the early diagnosis of diseases. As being a difficult task (even for experienced doctors), early diagnosis of diseases poses several challenges that are not well solved by prior studies, including insufficient training data, dynamic and complex signs of complications and trade-off between earliness and accuracy. To address these challenges, we propose a Reinforced Siamese network with Domain knowledge regularization approach, namely RSD, to achieve high performance for early diagnosis. The RSD approach consists of a diagnosis module and a control module. The diagnosis module adopts any EHR Encoder as a basic framework to extract representations, and introduces two improved training strategies. To overcome the insufficient sample problem, we design a Siamese network architecture to enhance the model learning. Furthermore, we propose a domain knowledge regularization strategy to guide the model learning with domain knowledge. Based on the diagnosis module, our control module learns to automatically determine whether making a disease alert to the patients based on the diagnosis results. Through carefully designed architecture, rewards and policies, it is able to effectively balance earliness and accuracy for diagnosis. Experimental results have demonstrated the effectiveness of our approach on both diagnosis prediction and early diagnosis. We also perform extensive analysis experiments to verify the robustness of the proposed approach.
Shoe Size Resolution in Search Queries and Product Listings using Knowledge Graphs
Authors: Petar Ristoski, Aritra Mandal, Simon Becker, Anu Mandalam, Ethan Hart, Sanjika Hewavitharana, Zhe Wu, Qunzhi Zhou
Venue: CIKM
Year: 2022

The Fashion domain is one of the most profitable domains in most of the e-commerce shops, shoes being one of the top-selling categories within this domain. When shopping for shoes, one of the most important aspects for the buyers is the shoe size. Shoe size charts differ between different brands, geographical regions, genders and age groups. Not providing some of these details, as a buyer or a seller, could lead to a query intent to inventory mismatch and reduced or wrong search results. Furthermore, buying the wrong shoe size is one of the top reasons for product returns, which causes shipping delays and loss in revenue. To address this issue, we propose an approach for shoe size resolution and normalization in search queries and product listings using Knowledge Graphs.
DBinsight: A Tool for Interactively Understanding the Query Processing Pipeline in RDBMSs
Authors: Ying Rong, Hui Li, Kankan Zhao, Xiyue Gao, Jiangtao Cui
Venue: CIKM
Year: 2022

Given an sql, a rdbms performs a series of operations to generate a Query Execution Plan (qep), which tells how the results will be collected and returned eventually. The whole pipeline for obtaining the qep is the core functionality of a rdbms, thus is definitely a fundamental knowledge that must be acquired by database learners and junior engineers of any rdbms. Unfortunately, though the majority of rdbmss provide EXPLAIN statement to show the qep, general users cannot see how/why these plans are generated. The only way for learning that is to turn to the textbook, which contains limited number of predefined examples accordingly. However, they are too sketchy to allow us to have a hand-on experience in practice. In this work, we present a general framework, DBinsight, that unveils the query processing pipeline visually at each phase during the processing pipeline, including parsing, translating, query optimization, etc. Considering that the underlying designs and optimization strategies of rdbmss are different, in DBinsight we present an SQPProfile interface, such that heterogeneous data structures in various rdbmss are normalized to a uniform format. Thanks to that, in DBinsight we only need to focus on offering the presentation and interaction functionalities based on the uniformed SQPProfile, and do not need to worry about the difference in the underlying rdbmss.
Deep Extreme Mixture Model for Time Series Forecasting
Authors: Abilasha S, Sahely Bhadra, Ahmed Zaheer Dadarkar, Deepak P
Venue: CIKM
Year: 2022

Time Series Forecasting (TSF) has been a topic of extensive research, which has many real world applications such as weather prediction, stock market value prediction, traffic control etc. Many machine learning models have been developed to address TSF, yet, predicting extreme values remains a challenge to be effectively addressed. Extreme events occur rarely, but tend to cause a huge impact, which makes extreme event prediction important. Assuming light tailed distributions, such as Gaussian distribution, on time series data does not do justice to the modeling of extreme points. To tackle this issue, we develop a novel approach towards improving attention to extreme event prediction. Within our work, we model time series data distribution, as a mixture of Gaussian distribution and Generalized Pareto distribution (GPD). In particular, we develop a novel Deep eXtreme Mixture Model (DXtreMM) for univariate time series forecasting, which addresses extreme events in time series. The model consists of two modules: 1) Variational Disentangled Auto-encoder (VD-AE) based classifier and 2) Multi Layer Perceptron (MLP) based forecaster units combined with Generalized Pareto Distribution (GPD) estimators for lower and upper extreme values separately. VD-AE Classifier model predicts the possibility of occurrence of an extreme event given a time segment, and forecaster module predicts the exact value. Through extensive set of experiments on real-world datasets we have shown that our model performs well for extreme events and is comparable with the existing baseline methods for normal time step forecasting.
CAVE: Correcting Attribute Values in E-commerce Profiles
Authors: Kassem Sabeh, Mouna Kacimi, Johann Gamper
Venue: CIKM
Year: 2022

Attribute value extraction from product profiles is essential for many applications such as product retrieval, comparison, and recommendation. While existing techniques focus mainly on the extraction task, none of them deals with the problem of correcting wrong attribute values. In this paper we propose CAVE, a novel system for attribute correction and enrichment using the Question Answering (QA) paradigm. CAVE learns information from both titles and attribute tables, using encoder and language models to correct attribute values. It also has the capability to enrich existing product descriptions with new attribute values extracted from titles. To the best of our knowledge, CAVE is the first system that allows users to experiment with a number of powerful QA models and compare their performances on attribute values correction using real-word datasets.
SCC - A Test Collection for Search in Chat Conversations
Authors: Ismail Sabei, Ahmed Mourad, Guido Zuccon
Venue: CIKM
Year: 2022

We present SCC, a test collection for evaluating search in chat conversations. Chat applications such as Slack, WhatsApp and Wechat have become popular communication methods. Typical search requirements in these applications revolve around the task of known item retrieval, i.e. find information that the user has previously experienced in their chats. However, the search capabilities of these chat applications are often very basic. Our collection aims to support new research into building effective methods for chat conversations search. We do so by building a collection with 114 known item retrieval topics for searching over 437,893 Slack chat messages. An important aspect when searching through conversations is the unit of indexing (indexing granularity), e.g., it being a single message vs. an entire conversation. To support researchers to investigate this aspect and its influence on retrieval effectiveness, the collection has been processed with conversation disentanglement methods: these mark cohesive segments in which each conversation consists of messages whose senders interact with each other regarding a specific event or topic. This results in a total of 38,955 multi-participant conversations being contained in the collection. Finally, we also provide a set of baselines with related empirical evaluation, including traditional bag-of-words methods and zero-shot neural methods, at both indexing granularity levels.
A Model-Centric Explainer for Graph Neural Network based Node Classification
Authors: Sayan Saha, Monidipa Das, Sanghamitra Bandyopadhyay
Venue: CIKM
Year: 2022

Graph Neural Networks (GNNs) learn node representations by aggregating a node's feature vector with its neighbors. They perform well across a variety of graph tasks. However, to enhance the reliability and trustworthiness of these models during use in critical scenarios, it is of essence to look into the decision making mechanisms of these models rather than treating them as black boxes. Our model-centric method gives insight into the kind of information learnt by GNNs about node neighborhoods during the task of node classification. We propose a neighborhood generator as an explainer that generates optimal neighborhoods to maximize a particular class prediction of the trained GNN model. We formulate neighborhood generation as a reinforcement learning problem and use a policy gradient method to train our generator using feedback from the trained GNN-based node classifier. Our method provides intelligible explanations of learning mechanisms of GNN models on synthetic as well as real-world datasets and even highlights certain shortcomings of these models.
Twin Papers: A Simple Framework of Causal Inference for Citations via Coupling
Authors: Ryoma Sato, Makoto Yamada, Hisashi Kashima
Venue: CIKM
Year: 2022

The research process includes many decisions, e.g., how to entitle and where to publish the paper. In this paper, we introduce a general framework for investigating the effects of such decisions. The main difficulty in investigating the effects is that we need to know counterfactual results, which are not available in reality. The key insight of our framework is inspired by the existing counterfactual analysis using twins, where the researchers regard twins as counterfactual units. The proposed framework regards a pair of papers that cite each other as twins. Such papers tend to be parallel works, on similar topics, and in similar communities. We investigate twin papers that adopted different decisions, observe the progress of the research impact brought by these studies, and estimate the effect of decisions by the difference in the impacts of these studies. We release our code and data, which we believe are highly beneficial owing to the scarcity of the dataset on counterfactual studies.
Measuring and Comparing the Consistency of IR Models for Query Pairs with Similar and Different Information Needs
Authors: Procheta Sen, Sourav Saha, Debasis Ganguly, Manisha Verma, Dwaipayan Roy
Venue: CIKM
Year: 2022

A widespread use of supervised ranking models has necessitated an investigation on how consistent their outputs align with user expectations. While a match between the user expectations and system outputs can be sought at different levels of granularity, we study this alignment for search intent transformation across a pair of queries. Specifically, we propose a consistency metric, which for a given pair of queries - one reformulated from the other with at least one term in common, measures if the change in the set of the top-retrieved documents induced by this reformulation is as per a user's expectation. Our experiments led to a number of observations, such as DRMM (an early interaction based IR model) exhibits better alignment with set-level user expectations, whereas transformer-based neural models (e.g., MonoBERT) agree more consistently with the content and rank-based expectations of overlap.
Spatial-Temporal Identity: A Simple yet Effective Baseline for Multivariate Time Series Forecasting
Authors: Zezhi Shao, Zhao Zhang, Fei Wang, Wei Wei, Yongjun Xu
Venue: CIKM
Year: 2022

Multivariate Time Series (MTS) forecasting plays a vital role in a wide range of applications. Recently, Spatial-Temporal Graph Neural Networks (STGNNs) have become increasingly popular MTS forecasting methods due to their state-of-the-art performance. However, recent works are becoming more sophisticated with limited performance improvements. This phenomenon motivates us to explore the critical factors of MTS forecasting and design a model that is as powerful as STGNNs, but more concise and efficient. In this paper, we identify the indistinguishability of samples in both spatial and temporal dimensions as a key bottleneck, and propose a simple yet effective baseline for MTS forecasting by attaching Spatial and Temporal IDentity information (STID), which achieves the best performance and efficiency simultaneously based on simple Multi-Layer Perceptrons (MLPs). These results suggest that we can design efficient and effective models as long as they solve the indistinguishability of samples, without being limited to STGNNs.
A Graph-based Spatiotemporal Model for Energy Markets
Authors: Swati Sharma, Srinivasan Iyengar, Shun Zheng, Kshitij Kapoor, Wei Cao, Jiang Bian, Shivkumar Kalyanaraman, John Lemmon
Venue: CIKM
Year: 2022

Energy markets enable matching supply and demand through inter- and intra-region electricity trading. Due to the interconnected nature of the energy markets, the supply-demand constraints in one region can impact prices in another connected region. To incorporate these spatiotemporal relationships, we propose a novel graph neural network architecture incorporating multidimensional time-series features to forecast price (node attribute) and energy flow (edge attribute) between regions simultaneously. To the best of our knowledge, this paper is the first attempt to combine node and edge level forecasting in energy markets. We show that our proposed approach has a mean absolute prediction percentage error of 12.8%, which significantly beats the state-of-the-art baseline techniques.
GALVIS: Visualization Construction through Example-Powered Declarative Programming
Authors: Leixian Shen, Enya Shen, Zhiwei Tai, Yun Wang, Yuyu Luo, Jianmin Wang
Venue: CIKM
Year: 2022

Declarative programmatic approaches are an essential modality for data visualization construction. Despite the powerful customization ability, declarative programming requires users to create charts from scratch, thus building a well-designed visualization is an effort-consuming process. In this paper, we propose leveraging examples to alleviate the problem. The use of examples plays a vital role in visualization design. Users can be allowed to browse through designs for inspiration and adapt them for their own visualizations. In this demo, we directly leverage the entire Vega/Vega-Lite example galleries as chart templates and introduce an authoring pipeline to conveniently instantiate templates with the user's data for extensible programmatic modifications. Finally, we build GALVIS, an example-powered declarative programming tool for visualization construction, enabling efficient declarative programming and retaining the full spectrum of Vega/Vega-Lite characteristics.
PubMed Author-assigned Keyword Extraction (PubMedAKE) Benchmark
Authors: Jiasheng Sheng, Zelalem Gero, Joyce C. Ho
Venue: CIKM
Year: 2022

With the ever-increasing abundance of biomedical articles, improving the accuracy of keyword search results becomes crucial for ensuring reproducible research. However, keyword extraction for biomedical articles is hard due to the existence of obscure keywords and the lack of a comprehensive benchmark. PubMedAKE is an author-assigned keyword extraction dataset that contains the title, abstract, and keywords of over 843,269 articles from the PubMed open access subset database. This dataset, publicly available on Zenodo, is the largest keyword extraction benchmark with sufficient samples to train neural networks. Experimental results using state-of-the-art baseline methods illustrate the need for developing automatic keyword extraction methods for biomedical literature.
Multi-task Generative Adversarial Network for Missing Mobility Data Imputation
Authors: Meihui Shi, Derong Shen, Yue Kou, Tiezheng Nie, Ge Yu
Venue: CIKM
Year: 2022

Mobility data collected from location-based social networks are imperative for user movement behaviour analysis and marketing strategy customization. However, due to personal privacy and temporary failure of GPS devices, mobility data suffer from missing data issues. The missing mobility data hide beneficial information that can lead to distorted data analysis. To this end, we propose a multi-task generative adversarial network, termed as MDI-MG, to mitigate the negative impact of missing mobility data by imputing possible missing records. Specifically, in MDI-MG, we first introduce region-awareness modelling to fully capture sequential dependencies. Then, the generator is designed as a multi-task network, which unifies two highly pertinent tasks, including the primary task and the auxiliary missing POI region imputation task. The joint training on the two tasks enhances presentation capabilities and brings additional benefits. Besides, we adopt a discriminator to evaluate the generated sequences. The generator and the discriminator are optimized with a minimax two-player game. Experiments on two real-world datasets show that, MDI-MG achieves better performance in terms of both imputation accuracy and effectiveness, compared with state-of-the-art methods.
CStory: A Chinese Large-scale News Storyline Dataset
Authors: Kaijie Shi, Xiaozhi Wang, Jifan Yu, Lei Hou, Juanzi Li, Jingtong Wu, Dingyu Yong, Jinghui Xiao, Qun Liu
Venue: CIKM
Year: 2022

In today's massive news streams, storylines can help us discover related event pairs and understand the evolution of hot events. Hence many efforts have been devoted to automatically constructing news storylines. However, the development of these methods is strongly limited by the size and quality of existing storyline datasets since news storylines are expensive to annotate as they contain a myriad of unlabeled relationships growing quadratically with the number of news events. Working around these difficulties, we propose a sophisticated pre-processing method to filter candidate news pairs by entity co-occurrence and semantic similarity. With the filter reducing annotation overhead, we construct CStory, a large-scale Chinese news storyline dataset, which contains 11,978 news articles, 112,549 manually labeled storyline relation pairs, and 49,832 evidence sentences for annotation judgment. We conduct extensive experiments on CStory using various algorithms and find that constructing news storylines is challenging even for pre-trained language models. Empirical analysis shows that the sample unbalance issue significantly influences model performance, which shall be the focus of future works. Our dataset is now publicly available at https://github.com/THU-KEG/CStory.
e-CLIP: Large-Scale Vision-Language Representation Learning in E-commerce
Authors: Wonyoung Shin, Jonghun Park, Taekang Woo, Yongwoo Cho, Kwangjin Oh, Hwanjun Song
Venue: CIKM
Year: 2022

Understanding vision and language representations of product content is vital for search and recommendation applications in e-commerce. As a backbone for online shopping platforms and inspired by the recent success in representation learning research, we propose a contrastive learning framework that aligns language and visual models using unlabeled raw product text and images. We present techniques we used to train large-scale representation learning models and share solutions that address domain-specific challenges. We study the performance using our pre-trained model as backbones for diverse downstream tasks, including category classification, attribute extraction, product matching, product clustering, and adult product recognition. Experimental results show that our proposed method outperforms the baseline in each downstream task regarding both single modality and multiple modalities.
Selective Tensorized Multi-layer LSTM for Orbit Prediction
Authors: Youjin Shin, Eun-Ju Park, Simon S. Woo, Okchul Jung, Daewon Chung
Venue: CIKM
Year: 2022

Although the collision of space objects not only incurs a high cost but also threatens human life, the risk of collision between satellites has increased, as the number of satellites has rapidly grown due to the significant interests in many space applications. However, it is not trivial to monitor the behavior of the satellite in real-time since the communication between the ground station and spacecraft is dynamic and sparse, and there is an increased latency due to the long distance. Accordingly, it is strongly required to predict the orbit of a satellite to prevent unexpected contingencies such as a collision. Therefore, the real-time monitoring and accurate orbit prediction are required. Furthermore, it is necessary to compress the prediction model, while achieving a high prediction performance in order to be deployable in the real systems. Although several machine learning and deep learning-based prediction approaches have been studied to address such issues, most of them have applied only basic machine learning models for orbit prediction without considering the size, running time, and complexity of the prediction model. In this research, we propose Selective Tensorized multi-layer LSTM (ST-LSTM) for orbit prediction, which not only improves the orbit prediction performance but also compresses the size of the model that can be applied in practical deployable scenarios. To evaluate our model, we use the real orbit dataset collected from the Korea Multi-Purpose Satellites (KOMPSAT-3 and KOMPSAT-3A) of the Korea Aerospace Research Institute (KARI) for 5 years. In addition, we compare our ST-LSTM to other machine learning-based regression models, LSTM, and basic tensorized LSTM models with regard to the prediction performance, model compression rate, and running time.