OpenRaiser/Intern-Atlas huggingface dataset:
SeiSMo: Semi-supervised Time Series Motif Discovery for Seismic Signal Detection
Authors: M. Ashraf Siddiquee, Zeinab Akhavan, Abdullah Mueen
Venue: CIKM
Year: 2019

Unlike semi-supervised clustering, classification and rule discovery; semi-supervised motif discovery is a surprisingly unexplored area in data mining. Semi-supervised Motif Discovery finds hidden patterns in long time series when a few arbitrarily known patterns are given. A naive approach is to exploit the known patterns and perform similarity search within a radius of the patterns. However, this method would find only similar shapes and would be limited in discovering new shapes. In contrast, traditional unsupervised motif discovery algorithms detect new shapes, while missing some patterns because the given information is not utilized. We propose a semi-supervised motif discovery algorithm that forms a nearest neighbor graph to identify chains of nearest neighbors from the given events. We demonstrate that the chains are likely to identify hidden patterns in the data. We have applied the method to find novel events in several geoscientific datasets more accurately than existing methods.
International Workshop on Model Selection and Parameter Tuning in Recommender Systems
Authors: Fikret Sivrikaya, Sahin Albayrak, Defu Lian
Venue: CIKM
Year: 2019

Recommender systems have strongly attracted the attention of the machine learning research community with prosperous real-life deployments in the last few decades. The performance and success of most applications developed in this domain highly depend on an elaborate selection of models and configuration of their hyperparameters. The international MoST-Rec 2019 workshop addresses the issues of algorithm selection and parameter tuning for recommender systems. The workshop aims to bring together researchers from the model selection and hyperparameter tuning community in the general scope of machine learning with researchers from the recommender systems community for discussing and exchanging recent advances and open challenges in the field.
Session-based Recommendation with Hierarchical Memory Networks
Authors: Bo Song, Yi Cao, Weifeng Zhang, Congfu Xu
Venue: CIKM
Year: 2019

The task of session-based recommendation aims to predict users' future interests based on anonymous historical sessions. Recent works have shown that memory models, which capture user preference from previous interaction sequence with long short-term or short-term memory, can lead to encouraging results in this problem. However, most existing memory models tend to regard each item as a memory unit, which neglect n-gram features and are insufficient to learn the user's feature-level preferences. In this paper, we aim to leverage n-gram features and model users' feature-level preferences in an explicit and effective manner. To this end, we present a memory model with multi-scale feature memory for session-based recommendation. A densely connected convolutional neural network (CNN) with short-cut path between upstream and downstream convolutional blocks is applied to build multi-scale features from item representations, and features in the same scale are combined with memory mechanism to capture users' feature-level preferences. Furthermore, attention is used to adaptively select users' multi-scale feature-level preferences for recommendation. Extensive experiments conducted on two benchmark datasets demonstrate the effectiveness of the proposed model in comparison with competitive baselines.
Adapting Visual Question Answering Models for Enhancing Multimodal Community Q&A Platforms
Authors: Avikalp Srivastava, Hsin Wen Liu, Sumio Fujita
Venue: CIKM
Year: 2019

Question categorization and expert retrieval methods have been crucial for information organization and accessibility in community question & answering (CQA) platforms. Research in this area, however, has dealt with only the text modality. With the increasingly multimodal nature of web content, we focus on extending these methods for CQA questions accompanied by images. Specifically, we leverage the success of representation learning for text and images in the visual question answering (VQA) domain and adapt the underlying concept and architecture for automated category classification and expert retrieval on image-based questions posted on Yahoo! Chiebukuro, the Japanese counterpart of Yahoo! Answers. To the best of our knowledge, this is the first work to tackle the multimodality challenge in CQA, and to adapt VQA models for tasks on a more ecologically valid source of visual questions. Our analysis of the differences between visual QA and community QA data drives our proposal of novel augmentations of an attention method tailored for CQA and use of auxiliary tasks for learning better grounding features. Our final model markedly outperforms the text-only and VQA model baselines for both tasks of classification and expert retrieval on real-world multimodal CQA data.
Personalized Route Description Based On Historical Trajectories
Authors: Han Su, Guanglin Cong, Wei Chen, Bolong Zheng, Kai Zheng
Venue: CIKM
Year: 2019

The turn-by-turn route descriptions provided in the existing navigation applications are exclusively derived from underlying road network topology information, i.e., the connectivity of edges to each other. Therefore, the turn-by-turn route descriptions are simplified as metric translation of physical world (e.g. distance/time to turn) to spoken language. Such translation that ignores human cognition of the geographic space, is frequently verbose and redundant for the drivers who have knowledge of the geographical areas. In this paper, we study a Personalized Route Description system dubbed PerRD-with which the goal is to generate more customized and intuitive route descriptions based on user generated content. PerRD utilizes a wealth of user generated historical trajectory data to extract frequently visited routes in the road network. The extracted information is used to make cognitive customized route description for each user. We formalize this task as a problem of finding the optimal partition for a given route that maximizes the familiarity while minimizing the number of partitions, and finding a proper sentence to describe each partition. For empirical study, our solution is applied to three trajectory datasets and users' real experiences to evaluate the performance and effectiveness of PerRD.
MithraLabel: Flexible Dataset Nutritional Labels for Responsible Data Science
Authors: Chenkai Sun, Abolfazl Asudeh, H. V. Jagadish, Bill Howe, Julia Stoyanovich
Venue: CIKM
Year: 2019

Using inappropriate datasets for data science tasks can be harmful, especially for applications that impact humans. Targeting data ethics, we demonstrate MithraLabel, a system for generating task-specific information about a dataset, in the form of a set of visual widgets, as a flexible "nutritional label" that provides a user with information to determine the fitness of the dataset for the task at hand.
BERT4Rec: Sequential Recommendation with Bidirectional Encoder Representations from Transformer
Authors: Fei Sun, Jun Liu, Jian Wu, Changhua Pei, Xiao Lin, Wenwu Ou, Peng Jiang
Venue: CIKM
Year: 2019

Modeling users' dynamic preferences from their historical behaviors is challenging and crucial for recommendation systems. Previous methods employ sequential neural networks to encode users' historical interactions from left to right into hidden representations for making recommendations. Despite their effectiveness, we argue that such left-to-right unidirectional models are sub-optimal due to the limitations including: \begin enumerate* [label=series\itshape\alph*\upshape)] \item unidirectional architectures restrict the power of hidden representation in users' behavior sequences; \item they often assume a rigidly ordered sequence which is not always practical. \end enumerate* To address these limitations, we proposed a sequential recommendation model called BERT4Rec, which employs the deep bidirectional self-attention to model user behavior sequences. To avoid the information leakage and efficiently train the bidirectional model, we adopt the Cloze objective to sequential recommendation, predicting the random masked items in the sequence by jointly conditioning on their left and right context. In this way, we learn a bidirectional representation model to make recommendations by allowing each item in user historical behaviors to fuse information from both left and right sides. Extensive experiments on four benchmark datasets show that our model outperforms various state-of-the-art sequential models consistently.
What Can History Tell Us?
Authors: Ke Sun, Tieyun Qian, Hongzhi Yin, Tong Chen, Yiqi Chen, Ling Chen
Venue: CIKM
Year: 2019

Recommendation systems have been widely applied to many E-commerce and online social media platforms. Recently, sequential item recommendation, especially session-based recommendation, has aroused wide research interests. However, existing sequential recommendation approaches either ignore the historical sessions or consider all historical sessions without any distinction that whether the historical sessions are relevant or not to the current session, which motivates us to distinguish the effect of each historical session and identify relevant historical sessions for recommendation. In light of this, we propose a novel deep learning based sequential recommender framework for session-based recommendation, which takes Nonlocal Neural Network and Recurrent Neural Network as the main building blocks. Specifically, we design a two-layer nonlocal architecture to identify historical sessions that are relevant to the current session and learn the long-term user preferences mostly from these relevant sessions. Besides, we also design a gated recurrent unit (GRU) enhanced by the nonlocal structure to learn the short-term user preferences from the current session. Finally, we propose a novel approach to integrate both long-term and short-term user preferences in a unified way to facilitate training the whole recommender model in an end-to-end manner. We conduct extensive experiments on two widely used real-world datasets, and the experimental results show that our model achieves significant improvements over the state-of-the-art methods.
UA-CRNN: Uncertainty-Aware Convolutional Recurrent Neural Network for Mortality Risk Prediction
Authors: Qingxiong Tan, Andy Jinhua Ma, Mang Ye, Baoyao Yang, Huiqi Deng, Vincent Wai-Sun Wong, Yee-Kit Tse, Terry Cheuk-Fung Yip, Grace Lai-Hung Wong, Jessica Yuet-Ling Ching, Francis Ka-Leung Chan, Pong C. Yuen
Venue: CIKM
Year: 2019

Accurate prediction of mortality risk is important for evaluating early treatments, detecting high-risk patients and improving healthcare outcomes. Predicting mortality risk from the irregular clinical time series data is challenging due to the varying time intervals in the consecutive records. Existing methods usually solve this issue by generating regular time series data from the original irregular data without considering the uncertainty in the generated data, caused by varying time intervals. In this paper, we propose a novel Uncertainty-Aware Convolutional Recurrent Neural Network (UA-CRNN), which incorporates the uncertainty information in the generated data to improve the mortality risk prediction performance. To handle the complex clinical time series data with sub-series of different frequencies, we propose to incorporate the uncertainty information into the sub-series level rather than the whole time series data. Specifically, we design a novel hierarchical uncertainty-aware decomposition layer (UADL) to adaptively decompose time series into different sub-series and assign them proper weights according to their reliabilities. Experimental results on two real-world clinical datasets demonstrate that the proposed UA-CRNN method significantly outperforms state-of-the-art methods in both short-term and long-term mortality risk predictions.
Batch Mode Active Learning for Semantic Segmentation Based on Multi-Clue Sample Selection
Authors: Yao Tan, Liu Yang, Qinghua Hu, Zhibin Du
Venue: CIKM
Year: 2019

Large labeled datasets are required for training a powerful semantic segmentation model. However, it is very expensive to construct pixel-wise annotated images. In this work, we propose a general batch mode active learning algorithm for semantic segmentation which automatically selects important samples to be labeled for building a competitive classifier. In our approach the edge information of an image is first introduced as a new selecting clue of active learning, which can measure the essential information relevant to segmentation performance. In addition, we also incorporate the informativeness based on Query by Committee (QBC) and representativeness criteria in our algorithm. We combine three clues to select a batch of samples during each iteration. It is shown that the image edge information is significant for the active learning for semantic segmentation in the experiments. And we also demonstrate the performance of our method outperforms the state of the art active learning approaches on the datasets of CamVid, Stanford Background and PASCAL VOC 2012.
Deep Colorization by Variation
Authors: Zineng Tang
Venue: CIKM
Year: 2019

We propose an adversarial learning based model for image colorization in which we elaborately adapt image translation mechanism that are optimized according to the task. After developing approaches on improving the global and local quality of the image colorization by analyzing this processing made by network architecture and objective functions, we formulate a diverse map-ping from the gray scale images to colorful images by latent space variation within the model. At last, discussion on the theoretical framework for studying color information distribution and video colorization is given.
Achieve Privacy-Preserving Truth Discovery in Crowdsensing Systems
Authors: Jianchao Tang, Shaojing Fu, Ming Xu, Yuchuan Luo, Kai Huang
Venue: CIKM
Year: 2019

To solve the problem that the data collected in crowdsensing systems are not reliable, a large number of truth discovery protocols have been proposed. However, most of them neglect the privacy protection existing in crowdsensing systems. Some truth discovery protocols that consider privacy only provide limited privacy protection, such as only protecting the privacy of collected data. To bridge the gap, in this paper, we propose a more comprehensive privacy-preserving truth discovery protocol that can simultaneously protect the privacy of participants and truth results. Specifically, our protocol encrypts participants' observed data based on Paillier Homomorphic Cryptosystem. Then, through the interaction between two servers, we can calculate participants' weights and estimate the truth results in the encrypted domain. Moreover, based on the data perturbation technology, the privacy of sensitive data exchanged between the two servers is protected in our protocol. Theoretical analysis and experimental results demonstrate that our protocol can effectively protect the privacy of participants and truth results without losing the accuracy of truth results.
GMTL: A GART Based Multi-task Learning Model for Multi-Social-Temporal Prediction in Online Games
Authors: Jianrong Tao, Linxia Gong, Changjie Fan, Longbiao Chen, Dezhi Ye, Sha Zhao
Venue: CIKM
Year: 2019

Multi-social-temporal (MST) data, which represent multi-attributed time series corresponding to the entities in multi-relational social network series, are ubiquitous in real-world and virtual-world dynamic systems, such as online games. Predictions over MST data such as social time series prediction and temporal link weight prediction are of great importance but challenging. They are affected by many complex factors, including temporal characteristics, social characteristics, collaborative characteristics, task characteristics and the intrinsic causality between them. In this paper, we propose a graph attention recurrent network (GART) based multi-task learning model (GMTL) to fuse information across multiple social-temporal prediction tasks. Experiments on an MMORPG dataset demonstrate that GMTL outperforms the state-of-the-art baselines and can significantly improve performances of specific social-temporal prediction task with additional information from others. Our work has been deployed to several MMORPGs in practice and can also expand to many related multi-social-temporal prediction tasks in real-world applications. Case studies on applications for multi-social-temporal prediction show that GMTL produces great value in the actual business in NetEase Games.
Concept Drift Adaption for Online Anomaly Detection in Structural Health Monitoring
Authors: Hongda Tian, Nguyen Lu Dang Khoa, Ali Anaissi, Yang Wang, Fang Chen
Venue: CIKM
Year: 2019

Despite its success for anomaly detection in the scenario where only data representing normal behavior are available, one-class support vector machine (OCSVM) still has challenge in dealing with non-stationary data stream, where the underlying distributions of data are time-varying. Existing OCSVM-based online learning methods incrementally update the model to address the challenge, however, they solely rely on the location relationship between a test sample and error support vectors. To better accommodate normal behavior evolution, online anomaly detection in non-stationary data stream is formulated as a concept drift adaptation problem in this paper. It is proposed that OCSVM-based incremental learning is only performed in the case of a normal drift. For an incoming sample, its relative relationship with three sets of vectors in OCSVM, namely margin support vectors, error support vectors, and reserve vectors is fully utilized to estimate whether a normal drift is emerging. Extensive experiments in the field of structural health monitoring have been conducted and the results have shown that the proposed simple approach outperforms the existing OCSVM-based online learning algorithms for anomaly detection.
Time Series Prediction with Interpretable Data Reconstruction
Authors: Qiangxing Tian, Jinxin Liu, Donglin Wang, Ao Tang
Venue: CIKM
Year: 2019

Time series prediction plays a key role in wide applications and has been investigated for a couple of decades. Nevertheless, most of the prior works fail to identify the most effective frequency components of time series before passing through the prediction, which induces the drop of the performance. In this paper, we propose a novel predictor which integrates the sequence to sequence (seq2seq) model based on long short-term memory units (LSTM) with interpretable data reconstruction, where the learned hidden state is taken as a bridge. The reconstructor can effectively regularize data based on the learned frequency components and extract the effective components of time series. Moreover, we present an alternative training mechanism and a dedicated loss function to guarantee the success of prediction. Experimental results on extensive real-world datasets show a prominent superiority in comparison to state-of-the-art methods.
Aspect and Opinion Aware Abstractive Review Summarization with Reinforced Hard Typed Decoder
Authors: Yufei Tian, Jianfei Yu, Jing Jiang
Venue: CIKM
Year: 2019

In this paper, we study abstractive review summarization. Observing that review summaries often consist of aspect words, opinion words and context words, we propose a two-stage reinforcement learning approach, which first predicts the output word type from the three types, and then leverages the predicted word type to generate the final word distribution. Experimental results on two Amazon product review datasets demonstrate that our method can consistently outperform several strong baseline approaches based on ROUGE scores.
One-Class Active Learning for Outlier Detection with Multiple Subspaces
Authors: Holger Trittenbach, Klemens Böhm
Venue: CIKM
Year: 2019

Active learning for outlier detection involves users in the process, by asking them for annotations of observations, in the form of class labels. The usual assumption is that users can provide such feedback, regardless of the nature and the presentation of the results. This is a simplification, which may not hold in practice. To overcome it, we propose SubSVDD, a semi-supervised classifier, that learns decision boundaries in low-dimensional projections of the data. SubSVDD de-constructs the outlier classification so that users can comprehend and interpret results more easily. For active learning, SubSVDD features a new update mechanism that adjusts decision boundaries based on user feedback. In particular, it considers that outliers may only occur in some of the low-dimensional projections. We conduct systematic experiments to show the effectiveness of our approach. In a comprehensive benchmark, SubSVDD outperforms alternative approaches on several data sets.
Message Passing for Complex Question Answering over Knowledge Graphs
Authors: Svitlana Vakulenko, Javier David Fernandez Garcia, Axel Polleres, Maarten de Rijke, Michael Cochez
Venue: CIKM
Year: 2019

Question answering over knowledge graphs (KGQA) has evolved from simple single-fact questions to complex questions that require graph traversal and aggregation. We propose a novel approach for complex KGQA that uses unsupervised message passing, which propagates confidence scores obtained by parsing an input question and matching terms in the knowledge graph to a set of possible answers. First, we identify entity, relationship, and class names mentioned in a natural language question, and map these to their counterparts in the graph. Then, the confidence scores of these mappings propagate through the graph structure to locate the answer entities. Finally, these are aggregated depending on the identified question type. This approach can be efficiently implemented as a series of sparse matrix multiplications mimicking joins over small local subgraphs. Our evaluation results show that the proposed approach outperforms the state of the art on the LC-QuAD benchmark. Moreover, we show that the performance of the approach depends only on the quality of the question interpretation results, i.e., given a correct relevance score distribution, our approach always produces a correct answer ranking. Our error analysis reveals correct answers missing from the benchmark dataset and inconsistencies in the DBpedia knowledge graph. Finally, we provide a comprehensive evaluation of the proposed approach accompanied with an ablation study and an error analysis, which showcase the pitfalls for each of the question answering components in more detail.
Machine Learning on Graphs with Kernels
Authors: Michalis Vazirgiannis, Giannis Nikolentzos, Giannis Siglidis
Venue: CIKM
Year: 2019

Graphs are becoming a dominant structure in current information management with many domains involved, including social networks, chemistry, biology, etc. Many real-world problems require applying machine learning tasks to graph-structured data. Graph kernels have emerged as a promising approach for dealing with these tasks. A graph kernel is a symmetric, positive semidefinite function on the set of graphs. These functions extend the applicability of kernel methods to graphs. Graph kernels have attracted a lot of attention during the last 20 years. The considerable research activity that occurred in the field resulted in the development of dozens of kernels, each focusing on specific structural properties of graphs. The goal of this tutorial is to offer a comprehensive presentation of a wide range of graph kernels, and to describe their key applications. The tutorial will also offer to the participants hands-on experience in applying graph kernels to classification problems.