OpenRaiser/Intern-Atlas huggingface dataset:
Large-Scale Information Extraction under Privacy-Aware Constraints
Authors: Rajeev Gupta, Ranganath Kondapally
Venue: CIKM
Year: 2021

In this digital age, people spend a significant portion of their lives online and this has led to an explosion of personal data from users and their activities. Typically, this data is private and nobody else, except the user, is allowed to look at it. This poses interesting and complex challenges from scalable information extraction point of view: extracting information under privacy aware constraints where there is little data to learn from but need highly accurate models to run on large amount of data across different users. Anonymization of data is typically used to convert private data into publicly accessible data. But this may not always be feasible and may require complex differential privacy guarantees in order to be safe from any potential negative consequences. Other techniques involve building models on a small amount of seen (eyes-on) data and a large amount of unseen (eyes-off) data. In this tutorial, we use emails as representative private data to explain the concepts of scalable IE under privacy-aware constraints.
AdaSim: A Recursive Similarity Measure in Graphs
Authors: Masoud Reyhani Hamedani, Sang-Wook Kim
Venue: CIKM
Year: 2021

In the literature, various link-based similarity measures such as Adamic/Adar (in short Ada), SimRank, and random walk with restart (RWR) have been proposed. Contrary to SimRank and RWR, Ada is a non-recursive measure, which exploits the local graph structure in similarity computation. Motivated by Ada's promising results in various graph-related tasks, along with the fact that SimRank is a recursive generalization of the co -citation measure, in this paper, we propose AdaSim, a recursive similarity measure based on the Ada philosophy. Our AdaSim provides identical accuracy to that of Ada on the first iteration and it is applicable to both directed and undirected graphs. To accelerate our iterative form, we also propose a matrix form that is dramatically faster while providing the exact AdaSim scores. We conduct extensive experiments with five real-world datasets to evaluate both the effectiveness and efficiency of our AdaSim in comparison with those of existing similarity measures and graph embedding methods in the task of similarity computation of nodes. Our experimental results show that 1) AdaSim significantly improves the effectiveness of Ada and outperforms other competitors, 2) its efficiency is comparable to that of SimRank* while being better than the others, 3) AdaSim is not sensitive to the parameter tuning, and 4) similarity measures are better than embedding methods to compute similarity of nodes.
Multi-view Interaction Learning for Few-Shot Relation Classification
Authors: Yi Han, Linbo Qiao, Jianming Zheng, Zhigang Kan, Linhui Feng, Yifu Gao, Yu Tang, Qi Zhai, Dongsheng Li, Xiangke Liao
Venue: CIKM
Year: 2021

Conventional deep learning-based Relation Classification (RC) methods heavily rely on large-scale training dataset and fail to generalize to unseen classes when training data is scant. This work concentrates on RC tasks in few-shot scenarios in which models classify the unlabelled samples given only few labeled samples. Existing few-shot RC models consider the dataset as a series of individual instances and have not fully utilized interaction information among them. Interaction information is conducive to indicate the important areas and produce discriminating representations. So this paper proposes a novel interactive attention network (IAN) which uses inter-instance and intra-instance interactive information to classify the relations. Inter-instance interactive information is first introduced to solve the low-resource problem by capturing the semantic relevance between an instance pair. Intra-instance interactive information is then introduced to address the ambiguous relation classification issue by extracting the entity information inner an instance. Extensive numerical experimental results demonstrate the proposed method promotes the accuracy of down-stream task.
Unsupervised Cross-system Log Anomaly Detection via Domain Adaptation
Authors: Xiao Han, Shuhan Yuan
Venue: CIKM
Year: 2021

Log anomaly detection, which focuses on detecting anomalous log records, becomes an active research problem because of its importance in developing stable and sustainable systems. Currently, many unsupervised log anomaly detection approaches are developed to address the challenge of limited anomalous samples. However, collecting enough data to train an unsupervised model is not practical when the system is newly deployed online. To tackle this challenge, we propose a transferable log anomaly detection (LogTAD) framework that leverages the adversarial domain adaptation technique to make log data from different systems have a similar distribution so that the detection model is able to detect anomalies from multiple systems. Experimental results show that LogTAD can achieve high accuracy on cross-system anomaly detection by using a small number of logs from the new system.
GraphITE: Estimating Individual Effects of Graph-structured Treatments
Authors: Shonosuke Harada, Hisashi Kashima
Venue: CIKM
Year: 2021

Outcome estimation of treatments for individual targets is a crucial foundation for decision making based on causal relations. Most of the existing outcome estimation methods deal with binary or multiple-choice treatments; however, in some applications, the number of interventions can be very large, while the treatments themselves have rich information. In this study, we consider one important instance of such cases, that is, the outcome estimation problem of graph-structured treatments such as drugs. Due to the large number of possible interventions, the counterfactual nature of observational data, which appears in conventional treatment effect estimation, becomes a more serious issue in this problem. Our proposed method GraphITE (pronounced 'graphite') obtains the representations of the graph-structured treatments using graph neural networks, and also mitigates the observation biases by using the HSIC regularization that increases the independence of the representations of the targets and the treatments. In contrast with the existing methods, which cannot deal with "zero-shot" treatments that are not included in observational data, GraphITE can efficiently handle them thanks to its capability of incorporating graph-structured treatments. The experiments using the two real-world datasets show GraphITE outperforms baselines especially in cases with a large number of treatments.
Automatic Error Correction Using the Wikipedia Page Revision History
Authors: Md. Kamrul Hasan, Mohammad Mahdavi
Venue: CIKM
Year: 2021

Error correction is one of the most crucial and time-consuming steps of data preprocessing. State-of-the-art error correction systems leverage various signals, such as predefined data constraints or user-provided correction examples, to fix erroneous values in a semi-supervised manner. While these approaches reduce human involvement to a few labeled tuples, they still need supervision to fix data errors. In this paper, we propose a novel error correction approach to automatically fix data errors of dirty datasets. Our approach pretrains a set of error corrector models on correction examples extracted from the Wikipedia page revision history. It then fine-tunes these models on the dirty dataset at hand without any required user labels. Finally, our approach aggregates the fine-tuned error corrector models to find the actual correction of each data error. As our experiments show, our approach automatically fixes a large portion of data errors of various dirty datasets with high precision.
Learning Multiple Intent Representations for Search Queries
Authors: Helia Hashemi, Hamed Zamani, W. Bruce Croft
Venue: CIKM
Year: 2021

Representation learning has always played an important role in information retrieval (IR) systems. Most retrieval models, including recent neural approaches, use representations to calculate similarities between queries and documents to find relevant information from a corpus. Recent models use large-scale pre-trained language models for query representation. The typical use of these models, however, has a major limitation in that they generate only a single representation for a query, which may have multiple intents or facets. The focus of this paper is to address this limitation by considering neural models that support multiple intent representations for each query. Specifically, we propose the NMIR (Neural Multiple Intent Representations) model that can generate semantically different query intents and their appropriate representations. We evaluate our model on query facet generation using a large-scale dataset of real user queries sampled from the Bing search logs. We also provide an extrinsic evaluation of the proposed model using a clarifying question selection task. The results show that NMIR significantly outperforms competitive baselines.
Cformer: Semi-Supervised Text Clustering Based on Pseudo Labeling
Authors: Arezoo Hatefi, Xuan-Son Vu, Monowar Bhuyan, Frank Drewes
Venue: CIKM
Year: 2021

We propose a semi-supervised learning method called Cformer for automatic clustering of text documents in cases where clusters are described by a small number of labeled examples, while the majority of training examples are unlabeled. We motivate this setting with an application in contextual programmatic advertising, a type of content placement on news pages that does not exploit personal information about visitors but relies on the availability of a high-quality clustering computed on the basis of a small number of labeled samples. To enable text clustering with little training data, Cformer leverages the teacher-student architecture of Meta Pseudo Labels. In addition to unlabeled data, Cformer uses a small amount of labeled data to describe the clusters aimed at. Our experimental results confirm that the performance of the proposed model improves the state-of-the-art if a reasonable amount of labeled data is available. The models are comparatively small and suitable for deployment in constrained environments with limited computing resources. The source code is available at https://github.com/Aha6988/Cformer
Centerpoint Query Authentication
Authors: Magnus Haxen, Morten Raeburn, Peyman Afshani, Panagiotis Karras
Venue: CIKM
Year: 2021

The rise of online map services drives data owners to outsource spatial data to potentially untrusted database providers. Query results are provided along with verification objects that allow confirming their authenticity. Such authentication schemes have been proposed for several spatial and geometric queries, as well as for median queries in one dimension. However, to date, no authentication mechanism exists for centerpoint queries, which return a point lying in the middle of other points in multidimensional space. In this paper, we propose an authentication scheme for centerpoint queries, grounded on the algorithm for centerpoint queries on a finite planar set of points and authenticated aggregation R-trees and accompanying authenticated aggregation queries. We also provide methods for finding the centerpoint of a subset of the complete data set, and implement a range-based method. Our solution has a worst-case time-complexity of O(n log n) and space-complexity of O(n). Our experimental study confirms these claims.
IWILDS'21: Second International Workshop on Learning During Web Search
Authors: Anett Hoppe, Ran Yu, Irina R. Brich, Jiqun Liu
Venue: CIKM
Year: 2021

Web search is one of the most ubiquitous online activities and often used as a starting point to learn, i. e., to acquire or extend one's knowledge about certain topics or procedures. When learning by searching the Web, individuals are confronted with an unprecedented amount of information in various forms and varying quality. Thus, successful learning on the Web requires high degrees of self-regulation and should be supported by the adequate design of search, recommendation, and training tools. This creates a highly interdisciplinary research area at the intersection of information retrieval, human-computer interaction, psychology, and educational sciences. Search as Learning (SAL) research examines the relationships between querying, navigation, media consumption behavior, and the learning outcomes during Web search, how they can be measured, predicted, and supported. Building on the growing SAL research community, IWILDS provides an interdisciplinary forum in a workshop that includes keynotes, paper presentations, and discussion. This year, IWILDS'21 specifically invites submissions focussing on challenges caused by the Covid-19 pandemic, investigating, for instance, health-related information acquisition on the Web and the pandemic-induced digitalization of formal learning.
Stock Trend Prediction with Multi-granularity Data: A Contrastive Learning Approach with Adaptive Fusion
Authors: Min Hou, Chang Xu, Yang Liu, Weiqing Liu, Jiang Bian, Le Wu, Zhi Li, Enhong Chen, Tie-Yan Liu
Venue: CIKM
Year: 2021

Stock trend prediction plays a crucial role in quantitative investing. Given the prediction task on a certain granularity (e.g., daily trend), a large portion of existing studies merely leverage market data of the same granularity (e.g., daily market data). In financial investment scenarios, however, there exist amounts of finer-grained information (e.g., high-frequency data) that contain more detailed investment signals beyond the original granularity data. This motivates us to investigate how to leverage multi-granularity market data to enhance the accuracy of stock trend prediction. Some straightforward methods, such as concatenating finer-grained data as features or fusing with a model based on finer-grained features, may not lead to more precise stock trend prediction due to some unique challenges. First, the inconsistency of granularity between the target trend and finer-grained data could substantially increase optimization difficulty, such as the relative sparsity of the target trend compared with higher dimensions of finer-grained features. Moreover, the continuously changing financial market state could result in varying efficacy of heterogeneous multi-granularity information, which consequently requires a dynamic approach for proper fusion among them. In this paper, we propose the Contrastive Multi-Granularity Learning Framework (CMLF) to address these challenges. Particularly, we first design two novel contrastive learning objectives at the pre-training stage to address the inconsistency issue by constructing additional self-supervised signals relying on the inherent character of stock data. We also design a gate mechanism based on market-aware technical indicators to fuse the multi-granularity features at each time step adaptively. Extensive experiments on three real-world datasets show significant improvements of our approach over the state-of-the-art baselines on stock trend prediction and profitability in real investing scenarios.
Two-tier Graph Contextual Embedding for Cross-device User Matching
Authors: Hongren Huang, Shu Guo, Chen Li, Jiawei Sheng, Lihong Wang, Jianxin Li, Jing Liu, Shenghai Zhong
Venue: CIKM
Year: 2021

The cross-device user matching task is to identify the behavior-logs (i.e., behavior sequences) on multiple devices that belong to one real person. Due to its anonymous and long-term properties, most previous methods of learning behavior embeddings cannot effectively capture two important features in the sequences, namely high-order connections and long-range dependencies. To this end, we propose a novel framework called Two-tier Graph Contextual Embedding (TGCE) to solve the above problems simultaneously. In the first tier, we construct behavior evolutionary graphs (BEGs) for behavior sequences and design an order-preserving neighbor aggregation network to collectively model transitions of behaviors with their neighbors. As repeated behaviors can be grouped into single nodes, our model joints neighboring environments around behaviors in a collective way, and behavior embeddings can be enriched. In the second tier, we further build scaled shortcut graphs (SSGs) by refining BEGs with random walk-based edge addition, then a position-aware graph attention network is further imposed on SSGs to facilitate fast information propagation. As distant graph nodes can be directly connected by shortcut edges, we can further capture long-range dependencies. By stacking two graph tiers, our approach can obtain graph contextual embeddings for behaviors to further improve user matching. Experimental results on the benchmark dataset show that our model outperforms various baselines in the user matching task. Our code is released on https://github.com/13061051/TGCE_2021.
Entity-aware Collaborative Relation Network with Knowledge Graph for Recommendation
Authors: Ruoran Huang, Chuanqi Han, Li Cui
Venue: CIKM
Year: 2021

As the source of side information, knowledge graph (KG) plays a critical role in recommender systems. Recently, graph neural networks (GNN) have shown their technical advancements at boosting recommendation performances. Existing GNN-based models mainly focus on aggregation technique and regularization allocation, ignoring the rich entity-aware information hidden in the relation network of KG. In this paper, we explore the relational semantics at the granularity of entities behind a user-item interaction by leveraging knowledge graph, named Entity-aware Collaborative Relation Network (ECRN). Technically, we construct multiple meta-paths from users to entities based on the user-item interaction and item-entity connectivity to obtain user representation, while designing a relation-aware self-attention mechanism to aggregate collaborative signals of items. Empirical results on three benchmarks show that ECRN significantly outperforms state-of-the-art baselines.
LTPHM: Long-term Traffic Prediction based on Hybrid Model
Authors: Chuyin Huang, Weiyang Kong, Genan Dai, Yubao Liu
Venue: CIKM
Year: 2021

Traffic prediction is a classical spaial-temporal prediction problem with many real-world applications.In general, existing traffic prediction methods capture the complex spatial-temporal features by iterative mechanism or non-iterative mechanism. However, the iterative mechanism often causes the prediction error accumulation and the non-iterative mechanism is hard to capture the dynamic propagation information. The shortcomings of both mechanisms lead to their poor performance in long-term prediction tasks. Target at the shortcomings of existing methods, in this paper, we propose a novel deep learning framework called Long-term Traffic Prediction based on Hybrid Model (LTPHM), which is designed to simulate the dynamic transmission process of traffic information on the road network by connecting the prediction values of the current step with the next step. Each spatial-temporal module uses graph convolution (GCN) with an adaptive matrix to capture spatial dependence. Besides, we use Gated Dilated Convolution Networks (GDCN) and Gated Linear Unit convolution networks (GLU) to capture temporal dependence. Since LTPHM integrates the advantages of both iterative and non-iterative prediction, it can efficiently capture the complex and dynamic spatial-temporal features, especially the long-range temporal sequences. Experiments with three real-world traffic datasets demonstrate the effectiveness of our proposed model.
Spatio-Temporal-Social Multi-Feature-based Fine-Grained Hot Spots Prediction for Content Delivery Services in 5G Era
Authors: Shaoyuan Huang, Heng Zhang, Xiaofei Wang, Min Chen, Jianxin Li, Victor C. M. Leung
Venue: CIKM
Year: 2021

The arrival of 5G networks has extensively promoted the growth of content delivery services (CDSs). Understanding and predicting the spatio-temporal distribution of CDSs are beneficial to mobile users, Internet Content Providers and carriers. Conventional methods for predicting the spatio-temporal distribution of CDSs are mostly base-stations (BSs) centric, leading to weak generalization and spatio coarse-grained. To improve the spatio accuracy and generalization of modeling, we propose user-centric methods for CDSs spatio-temporal analysis. With geocoding and spatio-temporal graphs modeling algorithms, CDSs records collected from mobile devices are modeled as dynamic graphs with spatio-temporal attributes. Moreover, we propose a spatio-temporal-social multi-feature extraction framework for spatio fine-grained CDSs hot spots prediction. Specifically, an edge-enhanced graph convolutional block is designed to encode CDSs information based on the social relations and the spatio dependence features. Besides, we introduce the Long Short Term Memory (LSTM) to further capture the temporal dependence. Experiments on two real-world CDSs datasets verified the effectiveness of the proposed framework, and ablation studies are taken to evaluate the importance of each feature.
When is Nearest Neighbor Meaningful: Sequential Data
Authors: Aaron Hui, Byron J. Gao
Venue: CIKM
Year: 2021

Nearest neighbor search is a fundamental problem in data management and analytics with vast applications. However, a seminal paper by Beyer et al demonstrated the curse of dimensionality, where under certain conditions with high dimensionality, all the data points tend to be equidistant and thus the nearest neighbor problem is meaningless. This influential work has spawned a series of investigations of the concentration phenomenon, which, for the most part, are limited to the vector space. In this paper, we extend this investigation to sequence data, which do not have an inherent notion of dimensions or attributes. For similarity measures we consider the commonly used edit distance and longest common subsequence. We perform theoretical analysis and prove conditions under which sequences will concentrate. We also conduct experiments on synthetic data to verify the theoretical findings. Rather than the curse of dimensionality as previous studies demonstrate, we attempt to demonstrate the curse of length for sequential data.
Prohibited Item Detection on Heterogeneous Risk Graphs
Authors: Yugang Ji, Chuan Shi, Xiao Wang
Venue: CIKM
Year: 2021

Prohibited item detection, which aims to detect illegal items hidden on e-commerce platforms, plays a significant role in evading risks and preventing crimes for online shopping. While traditional solutions usually focus on mining evidence from independent items, they cannot effectively utilize the rich structural relevance among different items. A naive idea is to directly deploy existing supervised graph neural networks to learn node representations for item classification. However, the very few manually labeled items with various risk patterns introduce two essential challenges: (1) How to enhance the representations of enormous unlabeled items? (2) How to enrich the supervised information in this few-labeled but multiple-pattern business scenario? In this paper, we construct item logs as a Heterogeneous Risk Graph (HRG), and propose the novel Heterogeneous Self-supervised Prohibited item Detection model (HSPD) to overcome these challenges. HSPD first designs the heterogeneous self-supervised learning model, which treats multiple semantics as the supervision to enhance item representations. Then, it presents the directed pairwise labeling to learn the distance from candidates to their most relevant prohibited seeds, which tackles the binary-labeled multi-patterned risks. Finally, HSPD integrates with self-training mechanisms to iteratively expand confident pseudo labels for enriching supervision. The extensive offline and online experimental results on three real-world HRGs demonstrate that HSPD consistently outperforms the state-of-the-art alternatives.