OpenRaiser/Intern-Atlas huggingface dataset:
Stop&Hop: Early Classification of Irregular Time Series
Authors: Thomas Hartvigsen, Walter Gerych, Jidapa Thadajarassiri, Xiangnan Kong, Elke A. Rundensteiner
Venue: CIKM
Year: 2022

Early classification algorithms help users react faster to their machine learning model's predictions. Early warning systems in hospitals, for example, let clinicians improve their patients' outcomes by accurately predicting infections. While early classification systems are advancing rapidly, a major gap remains: existing systems do not consider irregular time series, which have uneven and often-long gaps between their observations. Such series are notoriously pervasive in impactful domains like healthcare. We bridge this gap and study early classification of irregular time series, a new setting for early classifiers that opens doors to more real-world problems. Our solution, Stop&Hop, uses a continuous-time recurrent network to model ongoing irregular time series in real time, while an irregularity-aware halting policy, trained with reinforcement learning, predicts when to stop and classify the streaming series. By taking real-valued step sizes, the halting policy flexibly decides exactly when to stop ongoing series in real time. This way, Stop&Hop seamlessly integrates information contained in the timing of observations, a new and vital source for early classification in this setting, with the time series values to provide early classifications for irregular time series. Using four synthetic and three real-world datasets, we demonstrate that Stop&Hop consistently makes earlier and more-accurate predictions than state-of-the-art alternatives adapted to this new problem. Our code is publicly available at https://github.com/thartvigsen/StopAndHop.
Change Detection for Local Explainability in Evolving Data Streams
Authors: Johannes Haug, Alexander Braun, Stefan Zürn, Gjergji Kasneci
Venue: CIKM
Year: 2022

As complex machine learning models are increasingly used in sensitive applications like banking, trading or credit scoring, there is a growing demand for reliable explanation mechanisms. Local feature attribution methods have become a popular technique for post-hoc and model-agnostic explanations. However, attribution methods typically assume a stationary environment in which the predictive model has been trained and remains stable. As a result, it is often unclear how local attributions behave in realistic, constantly evolving settings such as streaming and online applications. In this paper, we discuss the impact of temporal change on local feature attributions. In particular, we show that local attributions can become obsolete each time the predictive model is updated or concept drift alters the data generating distribution. Consequently, local feature attributions in data streams provide high explanatory power only when combined with a mechanism that allows us to detect and respond to local changes over time. To this end, we present CDLEEDS, a flexible and model-agnostic framework for detecting local change and concept drift. CDLEEDS serves as an intuitive extension of attribution-based explanation techniques to identify outdated local attributions and enable more targeted recalculations. In experiments, we also show that the proposed framework can reliably detect both local and global concept drift. Accordingly, our work contributes to a more meaningful and robust explainability in online machine learning.
Causal Intervention for Sentiment De-biasing in Recommendation
Authors: Ming He, Xin Chen, Xinlei Hu, Changshu Li
Venue: CIKM
Year: 2022

Biases and de-biasing in recommender systems have received increasing attention recently. This study focuses on a newly identified bias, i.e., sentiment bias, which is defined as the divergence in recommendation performance between positive users/items and negative users/items. Existing methods typically employ a regularization strategy to eliminate the bias. However, blindly fitting the data without modifying the training procedure would result in a biased model, sacrificing recommendation performance. In this study, we resolve the sentiment bias with causal reasoning. We develop a causal graph to model the cause-effect relationships in recommender systems, in which the sentiment polarity presented by review text acts as a confounder between user/item representations and observed ratings. The existence of confounders inspires us to go beyond conditional probability and embrace causal inference. To that aim, we use causal intervention in model training to remove the negative effect of sentiment bias. Furthermore, during model inference, we adjust the prediction score to produce personalized recommendations. Extensive experiments on five benchmark datasets validate that the deconfounded training can remove the sentiment bias and the inference adjustment is helpful to improve recommendation accuracy.
PROPN: Personalized Probabilistic Strategic Parameter Optimization in Recommendations
Authors: Pengfei He, Haochen Liu, Xiangyu Zhao, Hui Liu, Jiliang Tang
Venue: CIKM
Year: 2022

Real-world recommender systems usually consist of two phases. Predictive models in Phase I provide accurate predictions of users' actions on items, and Phase II is to aggregate the predictions withstrategic parameters to make final recommendations, which aim to meet multiple business goals, such as maximizing users' like rate and average engagement time. Though it is important to generate accurate predictions in Phase I, it is also crucial to optimize the strategic parameters in Phase II. Conventional solutions include manually tunning, Bayesian optimization, contextual multi-armed bandit optimization, etc. However, these methods either produce universal strategic parameters for all the users or focus on a deterministic solution, which leads to an undesirable performance. In this paper, we propose a personalized probabilistic solution for strategic parameter optimization. We first formulate the personalized probabilistic optimizing problem and compare its solution with deterministic and context-free solutions theoretically to show its superiority. We then introduce a novel Personalized pRObabilistic strategic parameter optimizing Policy Network (PROPN) to solve the problem. PROPN follows reinforcement learning architecture where a neural network serves as an agent that dynamically adjusts the distributions of strategic parameters for each user. We evaluate our model under the streaming recommendation setting on two public real-world datasets. The results show that our framework outperforms representative baseline methods.
BLUTune: Query-informed Multi-stage IBM Db2 Tuning via ML
Authors: Connor Henderson, Spencer Bryson, Vincent Corvinelli, Parke Godfrey, Piotr Mierzejewski, Jaroslaw Szlichta, Calisto Zuzarte
Venue: CIKM
Year: 2022

Modern data systems such as IBM Db2 have hundreds of system configuration parameters, ''knobs", which heavily influence the performance of business queries. Manual configuration, ''tuning," by experts is painstaking and time consuming. We propose a query informed tuning system called BLUTune which uses machine learning (ML)-deep reinforcement learning based on advantage actor critic neural networks-to tune configurations within defined resource constraints. We translate high-dimensional query execution plans (QEPs) into a low-dimensional embedding space (QEP2Vec) for input into the ML models. To scale to complex and large workloads, we bootstrap the training process through transfer learning. We first train our model based on the estimated cost of queries; we then fine-tune it based on actual query execution times. We demonstrate by an experimental study over various synthetic and real-world workloads BLUTune's efficiency and effectiveness.
Semi-supervised Continual Learning with Meta Self-training
Authors: Stella Ho, Ming Liu, Lan Du, Yunfeng Li, Longxiang Gao, Shang Gao
Venue: CIKM
Year: 2022

Continual learning (CL) aims to enhance sequential learning by alleviating the forgetting of previously acquired knowledge. Recent advances in CL lack consideration of the real-world scenarios, where labeled data are scarce and unlabeled data are abundant. To narrow this gap, we focus on semi-supervised continual learning (SSCL). We exploit unlabeled data under limited supervision in the CL setting and demonstrate the feasibility of semi-supervised learning in CL. In this work, we propose a novel method, namely Meta-SSCL, which combines meta-learning with pseudo-labeling and data augmentations to learn a sequence of semi-supervised tasks without catastrophic forgetting. Extensive experiments on CL benchmark text classification datasets show that our method achieves promising results in SSCL.
AutoMARS: Searching to Compress Multi-Modality Recommendation Systems
Authors: Duc Hoang, Haotao Wang, Handong Zhao, Ryan A. Rossi, Sungchul Kim, Kanak Mahadik, Zhangyang Wang
Venue: CIKM
Year: 2022

Web applications utilize Recommendation Systems (RS) to address the problem of consumer over-choices. Recent works have taken advantage of multi-modality or multi-view, input information (such as user interaction, images, texts, rating scores) to boost recommendation system performance compared with using single-modality information. However, the use of multi-modality input demands much higher computational cost and storage capacity. On the other hand, the real-world RS services usually have strict budgets on both time and space for a good customer experience. As a result, the model efficiency of multi-modality recommendation systems has gained increasing importance. While unfortunately, to the best of our knowledge, there is no existing study of a generic compression framework for multi-modality RS. In this paper, we investigate, for the first time, how to compress a multi-modality recommendation system with a fixed budget. Assuming that input information from different modalities are of unequal importance, a good compression algorithm should learn to automatically allocate different resource budgets to each input, based on their importance in maximally preserving recommendation efficacy. To this end, we leverage the tools of neural architecture search (NAS) and distillation and propose Auto Multi-modAlity Recommendation System (AutoMARS), a unified modality-aware model compression framework dedicated to multi-modality recommendation systems. We demonstrate the effectiveness and generality of AutoMARS by testing it on three different Amazon datasets of various sparsity. AutoMARS demonstrates superior multi-modality compression performance than previous state-of-the-art compression methods. For example on the Amazon Beauty dataset, we achieve on average a 20% higher accuracy over previous state-of-the-art methods, while enjoying 65% reduction over baselines. Codes are available at: https://github.com/VITA-Group/AutoMARS.
Can We Have Both Fish and Bear's Paw?: Improving Performance, Reliability, and both of them for Relation Extraction under Label Shift
Authors: Yu Hong, Zhixu Li, Jianfeng Qu, Jiaqing Liang, Yi Luo, Miyu Zhang, Yanghua Xiao, Wei Wang
Venue: CIKM
Year: 2022

Neural Relation Extraction (RE) models need large amounts of labeled data for effective training, which mainly comes from automatically labeling by Distant Supervision (DS). Though fast and easy, the label shift problem inevitably happens, i.e., the label distribution of DS-generated training set is quite different from that of the real world (i.e. test set). According to our observations, label shift not only leads to performance diminishment, but also hinders the reliability of DS-RE models by causing bad confidence estimation. In this paper, we make contributions by answering the following three questions: 1) How to improve performance of DS-RE models under label shift? 2) How to make sure their reliability under label shift? 3) How to improve both performance and reliability for DS-RE models under label shift? To the best of our knowledge, this is the first paper to study the performance as well as reliability of DS-RE models under label shift. Experiment results show significant improvements on two real-world datasets and six popular neural RE models, making a step further towards high-performance and reliable RE system under real-world label-shift conditions.
META-CODE: Community Detection via Exploratory Learning in Topologically Unknown Networks
Authors: Yu Hou, Cong Tran, Won-Yong Shin
Venue: CIKM
Year: 2022

The discovery of community structures in social networks has gained considerable attention as a fundamental problem for various network analysis tasks. However, due to privacy concerns or access restrictions, the network structure is often unknown, thereby rendering established community detection approaches ineffective without costly data acquisition. To tackle this challenge, we present META-CODE, a novel end-to-end solution for detecting overlapping communities in networks with unknown topology via exploratory learning aided by easy-to-collect node metadata. Specifically, META-CODE consists of three steps: 1) initial network inference, 2) node-level community-affiliation embedding based on graph neural networks (GNNs) trained by our new reconstruction loss, and 3) network exploration via community-affiliation-based node queries, where Steps 2 and 3 are performed iteratively. Experimental results demonstrate that META-CODE exhibits (a) superiority over benchmark methods for overlapping community detection, (b) the effectiveness of our training model, and (c) fast network exploration.
Extreme Systematic Reviews: A Large Literature Screening Dataset to Support Environmental Policymaking
Authors: Jingwen Hou, Xiaochen Wang, Jean-Jacques Dubois, R. Byron Rice, Amanda Haddock, Yue Wang
Venue: CIKM
Year: 2022

The United States Environmental Protection Agency (EPA) periodically releases Integrated Science Assessments (ISAs) that synthesize the latest research on each of six air pollutants to inform environmental policymaking. To guarantee the best possible coverage of relevant literature, EPA scientists spend months manually screening hundreds of thousands of references to identify a small proportion to be cited in an ISA. The challenge of extreme scale and the pursuit of maximum recall calls for effective machine-assisted approaches to reducing the time and effort required by the screening process. This work introduces the ISA literature screening dataset and the associated research challenges to the information and knowledge management community. Our pilot experiments show that combining multiple approaches in tackling this challenge is both promising and necessary. The dataset is available at https://catalog.data.gov/dataset/isa-literature-screening-dataset-v-1.
Cross-Domain Aspect Extraction using Transformers Augmented with Knowledge Graphs
Authors: Phillip Howard, Arden Ma, Vasudev Lal, Ana Paula Simões, Daniel Korat, Oren Pereg, Moshe Wasserblat, Gadi Singer
Venue: CIKM
Year: 2022

The extraction of aspect terms is a critical step in fine-grained sentiment analysis of text. Existing approaches for this task have yielded impressive results when the training and testing data are from the same domain. However, these methods show a drastic decrease in performance when applied to cross-domain settings where the domain of the testing data differs from that of the training data. To address this lack of extensibility and robustness, we propose a novel approach for automatically constructing domain-specific knowledge graphs that contain information relevant to the identification of aspect terms. We introduce a methodology for injecting information from these knowledge graphs into Transformer models, including two alternative mechanisms for knowledge insertion: via query enrichment and via manipulation of attention patterns. We demonstrate state-of-the-art performance on benchmark datasets for cross-domain aspect term extraction using our approach and investigate how the amount of external knowledge available to the Transformer impacts model performance.
A GPU-based Graph Pattern Mining System
Authors: Lin Hu, Lei Zou
Venue: CIKM
Year: 2022

Graph pattern mining (GPM) is getting increasingly important in recent years. Many graph pattern mining frameworks try to use universal primitives to deal with various graph pattern mining tasks. However, most of them suffer from unsatisfactory performance because of the exponential complexity of GPM. GPU is a new hardware with great parallelism, and many graph algorithms have achieved significant performance improvements on GPU. In this demo, we propose a graph pattern mining framework on GPU, called GAMMA. GAMMA proposes effective and flexible interfaces for users to implement their mining tasks conveniently. GPM has extensive intermediate results in parallel environments. We make full use of host memory to deal with large-scale graphs and extensive intermediate results. We also present several optimizations to process large graphs. GAMMA has great scalability and performance advantages compared with state-of-the-art graph mining works in experiments.
Memory Bank Augmented Long-tail Sequential Recommendation
Authors: Yidan Hu, Yong Liu, Chunyan Miao, Yuan Miao
Venue: CIKM
Year: 2022

The goal of sequential recommendation is to predict the next item that a user would like to interact with, by capturing her dynamic historical behaviors. However, most existing sequential recommendation methods do not focus on solving the long-tail item recommendation problem that is caused by the imbalanced distribution of item data. To solve this problem, we propose a novel sequential recommendation framework, named MASR (ie Memory Bank Augmented Long-tail Sequential Recommendation). MASR is an "Open-book'' model that combines novel types of memory banks and a retriever-copy network to alleviate the long-tail problem. During inference, the designed retriever-copy network retrieves related sequences from the training samples and copies the useful information as a cue to improve the recommendation performance on tail items. Two designed memory banks provide reference samples to the retriever-copy network by memorizing the historical samples appearing in the training phase. Extensive experiments have been performed on five real-world datasets to demonstrate the effectiveness of the proposed MASR model. The experimental results indicate that MASR consistently outperforms baseline methods in terms of recommendation performance on tail items.
AMinerGNN: Heterogeneous Graph Neural Network for Paper Click-through Rate Prediction with Fusion Query
Authors: Zepeng Huai, Zhe Wang, Yifan Zhu, Peng Zhang
Venue: CIKM
Year: 2022

Paper recommendation with user-generated keyword is to suggest papers that simultaneously meet user's interests and are relevant to the input keyword. This is a recommendation task with two queries, a.k.a. user ID and keyword. However, existing methods focus on recommendation according to one query, a.k.a. user ID, and are not applicable to solving this problem. In this paper, we propose a novel click-through rate (CTR) prediction model with heterogeneous graph neural network, called AMinerGNN, to recommend papers with two queries. Specifically, AMinerGNN constructs a heterogeneous graph to project user, paper, and keyword into the same embedding space by graph representation learning. To process two queries, a novel query attentive fusion layer is designed to recognize their importances dynamically and then fuse them as one query to build a unified and end-to-end recommender system. Experimental results on our proposed dataset and online A/B tests prove the superiority of AMinerGNN.
Causal Relationship over Knowledge Graphs
Authors: Hao Huang
Venue: CIKM
Year: 2022

Causality has been discussed for centuries, and the theory of causal inference over tabular data has been broadly studied and utilized in multiple disciplines. However, only a few works attempt to infer the causality while exploiting the meaning of the data represented in a data structure like knowledge graph. These works offer a glance at the possibilities of causal inference over knowledge graphs, but do not yet consider the metadata, e.g., cardinalities, class subsumption and overlap, and integrity constraints. We propose CareKG, a new formalism to express causal relationships among concepts, i.e., classes and relations, and enable causal queries over knowledge graphs using semantics of metadata. We empirically evaluate the expressiveness of CareKG in a synthetic knowledge graph concerning cardinalities, class subsumption and overlap, integrity constraints. Our initial results indicate that CareKG can represent and measure causal relations with some semantics which are uncovered by state-of-the-art approaches.
Pattern Adaptive Specialist Network for Learning Trading Patterns in Stock Market
Authors: Huiling Huang, Jianliang Gao, Cong Xu, Xiaoting Ying
Venue: CIKM
Year: 2022

Stock prediction is a challenging task due to the uncertainty of stock markets. Despite the success of previous works, most of them rely on the assumption that stock data follow the identically identical distribution while the existence of multiple trading patterns in stock market violates it, ignoring multiple patterns in stock market will inevitably lead to the performance decline, and the lack of pattern prior knowledge further hinders the learning of patterns. In this paper, we propose a novel training process Pattern Adaptive Training based on Optimal Transport (OT) to train a set of predictors specializing in diverse patterns while without any prior pattern knowledge and inconsistent assumption. Based on this process, we further mine the potential fitness rank among specialists and design the Pattern Adaptive Specialist Network (PASN) with proposed ranking based selector to choose appropriate specialist predictor for samples. Extensive experimental results show that our method achieves best IC and other metrics on real-world stock datasets.
System-Auditing, Data Analysis and Characteristics of Cyber Attacks for Big Data Systems
Authors: Liangyi Huang, Sophia Hall, Fei Shao, Arafath Nihar, Vipin Chaudhary, Yinghui Wu, Roger H. French, Xusheng Xiao
Venue: CIKM
Year: 2022

Using big data, distributed computing systems such as Apache Hadoop requires processing massive amount of data to support business and research applications. Thus, it is critical to ensure the cyber security of such systems. To better defend from advanced cyber attacks that pose threats to even well-protected enterprises, system-auditing based techniques have been adopted for monitoring system activities and assisting attack investigation. In this demo, we are building a system that collects system auditing logs from a big data system and performs data analysis to understand how system auditing can be used more effectively to assist attack investigation on big systems. We also built a demo application that detects unexpected file deletion and presents root causes for the deletion.