OpenRaiser/Intern-Atlas huggingface dataset:
On Continuously Matching of Evolving Graph Patterns
Authors: Qianzhen Zhang, Deke Guo, Xiang Zhao, Aibo Guo
Venue: CIKM
Year: 2019

An evolving pattern graph is defined by an initial pattern graph and a graph update stream consisting of edge insertions and deletions. Identifying and monitoring evolving graph patterns in the data graph is important in various application domains such as Cyberthreats surveillance. This motivates us to explore matching patterns with evolvement, and the investigation presents a novel algorithm \incepg for continuously matching of evolving patterns. Specially, we propose a concise representation \Index of partial matching solutions, and its execution model allows fast incremental maintenance. We also conceive an effective model for estimating step-wise cost of pattern evaluation to drive the matching process. Extensive experiments verify the superiority of \incepg.
GRIP: Multi-Store Capacity-Optimized High-Performance Nearest Neighbor Search for Vector Search Engine
Authors: Minjia Zhang, Yuxiong He
Venue: CIKM
Year: 2019

This paper presents GRIP, an approximate nearest neighbor (ANN) search algorithm for building vector search engine which makes heavy use of the algorithm. GRIP is designed to retrieve documents at large-scale based on their semantic meanings in a scalable way. It is both fast and capacity-optimized. GRIP combines new algorithmic and system techniques to collaboratively optimize the use of memory, storage, and computation. The contributions include: (1) The first hybrid memory-storage ANN algorithm that allows ANN to benefit from both DRAM and SSDs simultaneously; (2) The design of a highly optimized indexing scheme that provides both memory-efficiency and high performance; (3) A cost analysis and a cost function for evaluating the capacity improvements of ANN algorithms. GRIP achieves an order of magnitude improvements on overall system efficiency, significantly reducing the cost of vector search, while attaining equal or higher accuracy, compared with the state-of-the-art.
Deep Forest with LRRS Feature for Fine-grained Website Fingerprinting with Encrypted SSL/TLS
Authors: Ziqing Zhang, Cuicui Kang, Gang Xiong, Zhen Li
Venue: CIKM
Year: 2019

With the development of encryption protocol, such as Secure Sockets Layer (SSL) and Transport Layer Security (TLS), the traditional fingerprinting approaches based on packet content and special field are difficult to fingerprint the websites. Therefore, recent research imported machine learning algorithms to deal with this problem, and various features are extracted for the machine learning algorithms. However, previous approaches of fingerprinting encrypted websites are based on HTTP/1.1, which are not applicable to the widely used HTTP/2. In addition, most of the work only fingerprints the home page of each website, but in fact, users also visit other web pages of the website. To solve the feature compatibility problem, we propose to use the local request and response sequence (LRRS) as features. LRRS can represent the patterns of the encrypted Internet traffic not only based on HTTP/1.1 but also based on HTTP/2 using local packet sequences. In order to fingerprint different web pages in the same website, we import Deep Forest to extract fine-grained features. It utilizes a convolution structure to make full use of LRRS sequential features and multi-layer structure to enhance the ability of feature representation. The experimental results show the proposed algorithm has achieved the best overall performance on four datasets. Especially on the bidirectional encrypted traffic dataset with HTTP/2, the proposed approach achieved 55% higher of f1 score than the state-of-the-art method KFP with Random Forest.
Towards the Gradient Vanishing, Divergence Mismatching and Mode Collapse of Generative Adversarial Nets
Authors: Zhaoyu Zhang, Changwei Luo, Jun Yu
Venue: CIKM
Year: 2019

Generative adversarial network (GAN) is a powerful generative model. However, it suffers from gradient vanishing, divergence mismatching and mode collapse. To overcome these problems, we propose a novel GAN, which consists of one generator G and two discriminators (D1, D2). Focusing on the gradient vanishing, Spectral Normalization (SN) and ResBlock are first adopted in D1 and D2. Then, Scaled Exponential Linear Units (SELU) is adopted at last half layers of D2 to further address the problem. To divergence mismatching, relativistic discriminator is adopted in our GAN to make the loss function minimization in the training of generator equal to the theoretical divergence minimization. Concentrating on the mode collapse, D1 rewards high scores for the samples from the data distribution, while D2 favors the samples from the generator conversely. In addition, the minibatch discrimination is adopted in D1 to further address the problem. Extensive experiments on CIFAR-10/100 and ImageNet datasets demonstrate that our GAN can obtain the highest inception score (IS) and lowest Frechet Inception Distance (FID) compared with other state-of-the-art GANs.
Job2Vec: Job Title Benchmarking with Collective Multi-View Representation Learning
Authors: Denghui Zhang, Junming Liu, Hengshu Zhu, Yanchi Liu, Lichen Wang, Pengyang Wang, Hui Xiong
Venue: CIKM
Year: 2019

Job Title Benchmarking (JTB) aims at matching job titles with similar expertise levels across various companies. JTB could provide precise guidance and considerable convenience for both talent recruitment and job seekers for position and salary calibration/prediction. Traditional JTB approaches mainly rely on manual market surveys, which is expensive and labor intensive. Recently, the rapid development of Online Professional graph has accumulated a large number of talent career records, which provides a promising trend for data-driven solutions. However, it is still a challenging task since (1) the job title and job transition (job-hopping) data is messy which contains a lot of subjective and non-standard naming conventions for a same position (\eg,Programmer, Software Development Engineer, SDE, Implementation Engineer ), (2) there is a large amount of missing title/transition information, and (3) one talent only seeks limited numbers of jobs which brings the incompleteness and randomness for modeling job transition patterns. To overcome these challenges, we aggregate all the records to construct a large-scale Job Title Benchmarking Graph (Job-Graph), where nodes denote job titles affiliated with specific companies and links denote the correlations between jobs. We reformulate the JTB as the task of link prediction over the Job-Graph that matched job titles should have links. Along this line, we propose a collective multi-view representation learning method (Job2Vec) by examining the Job-Graph jointly in (1) graph topology view (the structure of relationships among job titles), (2) semantic view (semantic meaning of job descriptions), (3) job transition balance view (the numbers of bidirectional transitions between two similar-level jobs are close), and (4) job transition duration view (the shorter the average duration of transitions is, the more similar the job titles are). We fuse the multi-view representations in the encode-decode paradigm to obtain an unified optimal representations for the task of link prediction. Finally, we conduct extensive experiments to validate the effectiveness of our proposed method.
Context-Aware Ranking by Constructing a Virtual Environment for Reinforcement Learning
Authors: Junqi Zhang, Jiaxin Mao, Yiqun Liu, Ruizhe Zhang, Min Zhang, Shaoping Ma, Jun Xu, Qi Tian
Venue: CIKM
Year: 2019

Result ranking is one of the major concerns for Web search technologies. Most existing methodologies rank search results in descending order according to pointwise relevance estimation of single results. However, the dependency relationship between different search results are not taken into account. While search engine result pages contain more and more heterogenous components, a better ranking strategy should be a context-aware process and optimize result ranking globally. In this paper, we propose a novel framework which aims to improve context-aware listwise ranking performance by optimizing online evaluation metrics. The ranking problem is formalized as a Markov Decision Process (MDP) and solved with the reinforcement learning paradigm. To avoid the great cost to online systems during the training of the ranking model, we construct a virtual environment with millions of historical click logs to simulate the behavior of real users. Extensive experiments on both simulated and real datasets show that: 1) constructing a virtual environment can effectively leverage the large scale click logs and capture some important properties of real users. 2) the proposed framework can improve search ranking performance by a large margin.
PRNet: Outdoor Position Recovery for Heterogenous Telco Data by Deep Neural Network
Authors: Yige Zhang, Weixiong Rao, Kun Zhang, Mingxuan Yuan, Jia Zeng
Venue: CIKM
Year: 2019

Recent years have witnessed unprecedented amounts of telecommunication (Telco) data generated by Telco networks. For example, measurement records (MRs) are generated to report the connection states, e.g., received signal strength, between mobile devices and Telco networks. MR data have been widely used to precisely recover outdoor locations of mobile devices for the applications e.g., human mobility, urban planning and traffic forecasting. Existing works using first-order sequence models such as the Hidden Markov Model (HMM) attempt to capture the spatio-temporal locality in underlying mobility patterns for lower localization errors. Such HMM approaches typically assume stable mobility pattern of underlying mobile devices. Yet real MR datasets frequently exhibit heterogeneous mobility patterns due to mixed transportation modes of underlying mobile devices and uneven distribution of the positions associated with MR samples. To address this issue, we propose a deep neural network (DNN)-based position recovery framework, namely PRNet, which can ensemble the power of CNN, sequence model LSTM, and two attention mechanisms to learn local, short- and long-term spatio-temporal dependencies from input MR samples. Extensive evaluation on six datasets collected at three representative areas (core, urban, and suburban areas in Shanghai, China) indicates that PRNet greatly outperforms seven counterparts.
Recommendation-based Team Formation for On-demand Taxi-calling Platforms
Authors: Lingyu Zhang, Tianshu Song, Yongxin Tong, Zimu Zhou, Dan Li, Wei Ai, Lulu Zhang, Guobin Wu, Yan Liu, Jieping Ye
Venue: CIKM
Year: 2019

On-demand taxi-calling platforms often ignore the social engagement of individual drivers. The lack of social incentives impairs the work enthusiasms of drivers and will affect the quality of service. In this paper, we propose to form teams among drivers to promote participation. A team consists of a leader and multiple members, which acts as the basis for various group-based incentives such as competition. We define the Recommendation-based Team Formation (RTF) problem to form as many teams as possible while accounting for the choices of drivers. The RTF problem is challenging. It needs both accurate recommendation and coordination among recommendations, since each driver can be in at most one team. To solve the RTF problem, we devise a Recommendation-Matrix-Based Framework (RMBF). It first estimates the acceptance probability of recommendations and then derives a recommendation matrix to maximize the number of formed teams from a global view. We conduct trace-driven simulations using real data covering over 64,000 drivers and deploy our solution on a large on-demand taxi-calling platform for online evaluations. Experimental results show that RMBF outperforms the greedy-based strategy by forming up to 20% and 12.4% teams in trace-driven simulations and online evaluations, and the drivers who form teams and are involved in the competition have more service time, number of finished orders and income.
Adaptive Feature Redundancy Minimization
Authors: Rui Zhang, Hanghang Tong, Yifan Hu
Venue: CIKM
Year: 2019

Most existing feature selection methods select the top-ranked features according to certain criterion. However, without considering the redundancy among the features, the selected ones are frequently highly correlated with each other, which is detrimental to the performance. To tackle this problem, we propose a framework regarding adaptive redundancy minimization (ARM) for the feature selection. Unlike other feature selection methods, the proposed model has the following merits: (1) The redundancy matrix is adaptively constructed instead of presetting it as the priori information. (2) The proposed model could pick out the discriminative and non-redundant features via minimizing the global redundancy of the features. (3) ARM can reduce the redundancy of the features from both supervised and unsupervised perspectives.
Robust Embedded Deep K-means Clustering
Authors: Rui Zhang, Hanghang Tong, Yinglong Xia, Yada Zhu
Venue: CIKM
Year: 2019

Deep neural network clustering is superior to the conventional clustering methods due to deep feature extraction and nonlinear dimensionality reduction. Nevertheless, deep neural network leads to a rough representation regarding the inherent relationship of the data points. Therefore, it is still difficult for deep neural network to exploit the effective structure for direct clustering. To address this issue, we propose a robust embedded deep K-means clustering (RED-KC) method. The proposed RED-KC approach utilizes the δ-norm metric to constrain the feature mapping process of the auto-encoder network, so that data are mapped to a latent feature space, which is more conducive to the robust clustering. Compared to the existing auto-encoder networks with the fixed prior, the proposed RED-KC is adaptive during the process of feature mapping. More importantly, the proposed RED-KC embeds the clustering process with the auto-encoder network, such that deep feature extraction and clustering can be performed simultaneously. Accordingly, a direct and efficient clustering could be obtained within only one step to avoid the inconvenience of multiple separate stages, namely, losing pivotal information and correlation. Consequently, extensive experiments are provided to validate the effectiveness of the proposed approach.
Autor3: Automated Real-time Ranking with Reinforcement Learning in E-commerce Sponsored Search Advertising
Authors: Yusi Zhang, Zhi Yang, Liang Wang, Li He
Venue: CIKM
Year: 2019

Sponsored search platforms rank the advertisements (ads) by a ranking function to determine the impression allocation and the charging price for the advertisers. To place ads optimally, it is highly desirable but remain challenging to adapt ranking function to ad traffic at both large-scale and fine granularity. In this paper, we propose an automatic adaptive auction system called Autor 3. Our system leverages the variability and correlation of ad traffic in a search session and models ranking ads in a session as a multi-step decision-making problem. With effective yet lightweight abstractions of auction states and ranking actions, Autor3 builds a reinforcement learning (RL) framework to learn the ranking decision at the fine granularity of page views (i.e., impressions) over the large-scale auction volume. Our offline experiments show that our method considering sequential decision are superior to those that do not. We deployed Autor3 to process the billion-scale impressions per day in Taobao, the largest e-commerce platform in China. Using online A/B test and a subsequent full-scale deployment, we show that both the Revenue-Per-Mille (RPM) and Click-Through-Rates (CTRs) are improved comparing to the previous keyword-level approach used in Taobao's live production environment.
Hashing Graph Convolution for Node Classification
Authors: Wenting Zhao, Zhen Cui, Chunyan Xu, Chengzheng Li, Tong Zhang, Jian Yang
Venue: CIKM
Year: 2019

Convolution on graphs has aroused great interest in AI due to its potential applications to non-gridded data. To bypass the influence of ordering and different node degrees, the summation/average diffusion/aggregation is often imposed on local receptive field in most prior works. However, the collapsing into one node in this way tends to cause signal entanglements of nodes, which would result in a sub-optimal feature and decrease the discriminability of nodes. To address this problem, in this paper, we propose a simple but effective Hashing Graph Convolution (HGC) method by using global-hashing and local-projection on node aggregation for the task of node classification. In contrast to the conventional aggregation with a full collision, the hash-projection can greatly reduce the collision probability during gathering neighbor nodes. Another incidental effect of hash-projection is that the receptive field of each node is normalized into a common-size bucket space, which not only staves off the trouble of different-size neighbors and their order but also makes a graph convolution run like the standard shape-gridded convolution. Considering the few training samples, also, we introduce a prediction-consistent regularization term into HGC to constrain the score consistency of unlabeled nodes in the graph. HGC is evaluated on both transductive and inductive experimental settings and achieves new state-of-the-art results on all datasets for node classification task. The extensive experiments demonstrate the effectiveness of hash-projection.
GRAPHENE: A Precise Biomedical Literature Retrieval Engine with Graph Augmented Deep Learning and External Knowledge Empowerment
Authors: Sendong Zhao, Chang Su, Andrea Sboner, Fei Wang
Venue: CIKM
Year: 2019

Effective biomedical literature retrieval (BLR) plays a central role inprecision medicine informatics. In this paper, we propose GRAPHENE,which is a deep learning based framework for precise BLR. GRAPHENEconsists of three main different modules 1) graph-augmented doc-ument representation learning; 2) query expansion and represen-tation learning and 3) learning to rank biomedical articles. Thegraph-augmented document representation learning module con-structs a document-concept graph containing biomedical conceptnodes and document nodes so that global biomedical related con-cept from external knowledge source can be captured, which isfurther connected to a BiLSTM so both local and global topics canbe explored. Query expansion and representation learning moduleexpands the query with abbreviations and different names, and thenbuilds a CNN-based model to convolve the expanded query andobtain a vector representation for each query. Learning to rank min-imizes a ranking loss between biomedical articles with the queryto learn the retrieval function. Experimental results on applyingour system to TREC Precision Medicine track data are provided todemonstrate its effectiveness.
CurrentClean: Interactive Change Exploration and Cleaning of Stale Data
Authors: Zheng Zheng, Tri Minh Quach, Ziyi Jin, Fei Chiang, Mostafa Milani
Venue: CIKM
Year: 2019

Enterprises often assume their data is up-to-date, where the presence of a timestamp in the recent past qualifies the data as current. However, entities modeled in the data experience varying rates of change that influence data currency. We argue that data currency is a relative notion based on individual spatio-temporal update patterns, and these patterns can be learned and predicted. We develop CurrentClean, a probabilistic system for identifying and cleaning stale values, and enables a user to interactively explore change in her data. Our system provides a Web-based user-interface, and a backend infrastructure that learns update correlations among cell values in a database to infer and repair stale values. Our demonstration provides two motivating scenarios that highlight change exploration, and cleaning features using clinical, and sensor data from a data centre enterprise.
Author Set Identification via Quasi-Clique Discovery
Authors: Yuyan Zheng, Chuan Shi, Xiangnan Kong, Yanfang Ye
Venue: CIKM
Year: 2019

Author identification based on heterogeneous bibliographic networks, which is to identify potential authors given an anonymous paper, has been studied in recent years. However, most of the existing works merely consider the relationship between authors and anonymous papers, while ignore the relationships between authors. In this paper, we take the relationships among authors into consideration to study the problem of author set identification, which is to identify an author set rather than an individual author related to an anonymous paper. The proposed problem has important applications to new collaborator discovery and group building. We propose a novel Author Set Identification approach, namely ASI. ASI first extracts a task-guided embedding to learn the low-dimensional representations of nodes in bibliographic network. And then ASI leverages the learned embedding to construct a weighted paper-ego-network, which contains anonymous paper and candidate authors. Finally, converting the optimal author set identification to the quasi-clique discovery in the constructed network, ASI utilizes a local-search heuristic mechanism under the guidance of the devised density function to find the optimal quasiclique. Extensive experiments on bibliographic networks demonstrate that ASI outperforms the state-of-art baselines in author set identification.
A Latent-Constrained Variational Neural Dialogue Model for Information-Rich Responses
Authors: Yanan Zheng, Yan Wang, Lijie Wen, Jianmin Wang
Venue: CIKM
Year: 2019

The variational neural models have achieved significant progress in dialogue generation. They are of encoder-decoder architecture, with stochastic latent variables learned at the utterance level. However, latent variables are usually approximated by factorized-form distributions, the value space of which is too large relative to latent features to be encoded, leading to the sparsity problem. As a result, little useful information is carried in latent representations, and generated responses tend to be non-committal and meaningless. To address it, we initially propose the Latent-Constrained Variational Neural Dialogue Model (LC-VNDM). It follows variational neural dialogue framework, with an utterance encoder, a context encoder and a response decoder hierarchically organized. Particularly, LC-VNDM uses a hierarchically-structured variational distribution form, which considers inter-dependencies between latent variables. Thus it defines a constrained latent value space, and prevents latent global features from being diluted. Therefore, latent representations sampled from it would carry richer global information to facilitate the decoding, generating meaningful responses. We conduct extensive experiments on three datasets using automatic evaluation and human evaluation. Experiments prove that LC-VNDM significantly outperforms the state-of-the-arts and can generate information-richer responses by learning a better-quality latent space.