OpenRaiser/Intern-Atlas huggingface dataset:
Synergizing Local and Global Models for Matrix Approximation
Authors: Chao Chen, Hao Zhang, Dongsheng Li, Junchi Yan, Xiaokang Yang
Venue: CIKM
Year: 2019

Ensemble matrix approximation (MA) methods have achieved promising performance in collaborative filtering, many of which perform matrix approximation on multiple submatrices of user-item ratings in parallel and then combine the predictions from the sub-models for higher efficiency. However, data partitioning could lead to suboptimal accuracy due to the lack of capturing structural information related to most or all users/items. This paper proposes a new ensemble learning framework, in which the local models and global models are synergetically updated from each other. This makes it possible to capture both local associations in user-item subgroups and global structures over all users and items. Experiments on three real-world datasets demonstrate that the proposed method outperforms six state-of-the-art methods in recommendation accuracy with decent scalability.
Fine-Grained Product Categorization in E-commerce
Authors: Hongshen Chen, Jiashu Zhao, Dawei Yin
Venue: CIKM
Year: 2019

E-commerce sites usually leverage taxonomies for better organizing products. The fine-grained categories, regarding the leaf categories in taxonomies, are defined by the most descriptive and specific words of products. Fine-grained product categorization remains challenging, due to blurred concepts of fine grained categories (i.e. multiple equivalent or synonymous categories), instable category vocabulary (i.e. the emerging new products and the evolving language habits), and lack of labelled data. To address these issues, we proposes a novel Neural Product Categorization model---NPC to identify fine-grained categories from the product content. NPC is equipped with a character-level convolutional embedding layer to learn the compositional word representations, and a spiral residual layer to extract the word context annotations capturing complex long range dependencies and structural information. To perform categorization beyond predefined categories, NPC categorizes a product by jointly recognizing categories from the product content and predicting categories from predefined category vocabularies. Furthermore, to avoid extensive human labors, NPC is able to adapt to weak labels, generated by mining the search logs, where the customers' behaviors naturally connect products with categories. Extensive experiments performed on a real e-commerce platform datasets illustrate the effectiveness of the proposed models.
EYRE 2019: 2nd International Workshop on EntitY REtrieval
Authors: Gong Cheng, Kalpa Gunaratna, Jun Wang
Venue: CIKM
Year: 2019

Entity retrieval has received increasing research attention from both the Information Retrieval (IR) and Semantic Web communities. This workshop series provides a platform where interdisciplinary studies of entity retrieval can be presented, and focused discussions can take place. We also organize two shared tasks related to entity retrieval. The 2nd International Workshop on EntitY REtrieval (EYRE 2019) was a half-day workshop co-located with the 28th ACM International Conference on Information and Knowledge Management (CIKM 2019) in Beijing, China.
Deploying Hash Tables on Die-Stacked High Bandwidth Memory
Authors: Xuntao Cheng, Bingsheng He, Eric Lo, Wei Wang, Shengliang Lu, Xinyu Chen
Venue: CIKM
Year: 2019

Die-stacked High Bandwidth Memory (HBM) is an emerging memory architecture that achieves much higher memory bandwidth with similar or lower memory access latency and smaller capacity, compared with main memories. Memory-intensive database algorithms may potentially benefit from these new features. Due to the small capacity of such die-stacked HBM, a hybrid memory architecture comprising both main memories and HBMs is promising for main-memory databases. As a starting point, we study a key data structure, hash tables, in such a hybrid memory architecture. In a large hash table distributed among multiple NUMA (non-uniform memory accesses) nodes and accessed by multiple CPU sockets, the data placement and memory access scheduling for workload balance are challenging due to the random memory accesses involved that are difficult to predict. In this work, we propose a deployment algorithm that first estimates the memory access cost and then places data in a way that exploits the hybrid memory architecture in a balanced manner. Evaluation results show that the proposed deployment is able to achieve up to three times performance improvement over the state-of-the-art NUMA-aware scheduling algorithms for hash joins in relational databases on present and simulated future hybrid memory architectures.
A Dynamic Default Prediction Framework for Networked-guarantee Loans
Authors: Dawei Cheng, Yiyi Zhang, Fangzhou Yang, Yi Tu, Zhibin Niu, Liqing Zhang
Venue: CIKM
Year: 2019

Commercial banks normally require Small and Medium Enterprises (SMEs) to provide their warranties when applying for a loan. If the borrower defaults, the guarantor is obligated to repay its loan. Such a guarantee system is designed to reduce delinquent risks, but may introduce a new dimension risk if more and more SMEs involve and subsequently form complex temporal networks. Monitoring the financial status of SMEs in these networks, and preventing or reducing systematic loan risk, is an area of great concern for both the regulatory commission and the banks. To allow possible actions to be taken in advance, this paper studies the problem of predicting repayment delinquency in the networked-guarantee loans. We propose a dynamic default prediction framework (DDPF), which preserves temporal network structures and loan behavior sequences in an end-to-end model. In particular, we design a gated recursive and attention mechanism to integrate both the loan behavior and network information. Then, we uncover risky warrant patterns by the learned weights, which effectively accelerate risk evaluation process. Finally, we conduct extensive experiments in a real-world loan risk control system to evaluate its performance, the results demonstrate the effectiveness of our proposed approach compared with state-of-the-art baselines.
Look before you Hop: Conversational Question Answering over Knowledge Graphs Using Judicious Context Expansion
Authors: Philipp Christmann, Rishiraj Saha Roy, Abdalghani Abujabal, Jyotsna Singh, Gerhard Weikum
Venue: CIKM
Year: 2019

Fact-centric information needs are rarely one-shot; users typically ask follow-up questions to explore a topic. In such a conversational setting, the user's inputs are often incomplete, with entities or predicates left out, and ungrammatical phrases. This poses a huge challenge to question answering (QA) systems that typically rely on cues in full-fledged interrogative sentences. As a solution, we develop CONVEX, an unsupervised method that can answer incomplete questions over a knowledge graph (KG) by maintaining conversation context using entities and predicates seen so far and automatically inferring missing or ambiguous pieces for follow-up questions. The core of our method is a graph exploration algorithm that judiciously expands a frontier to find candidate answers for the current question. To evaluate CONVEX, we release ConvQuestions, a crowdsourced benchmark with 11,200 distinct conversations from five different domains. We show that CONVEX: (i) adds conversational support to any stand-alone QA system, and (ii) outperforms state-of-the-art baselines and question completion strategies.
Accounting for Temporal Dynamics in Document Streams
Authors: Zhendong Chu, Renqin Cai, Hongning Wang
Venue: CIKM
Year: 2019

Textual information, such as news articles, social media, and online forum discussions, often comes in a form of sequential text streams. Events happening in the real world trigger a set of articles talking about them or related events over a period of time. In the meanwhile, even one event is fading out, another related event could raise public attention. Hence, it is important to leverage the information about how topics influence each other over time to obtain a better understanding and modeling of document streams. In this paper, we explicitly model mutual influence among topics over time, with the purpose to better understand how events emerge, fade and inherit. We propose a temporal point process model, referred to as Correlated Temporal Topic Model (CoTT), to capture the temporal dynamics in a latent topic space. Our model allows for efficient online inference, scaling to continuous time document streams. Extensive experiments on real-world data reveal the effectiveness of our model in recovering meaningful temporal dependency structure among topics and documents.
AutoGRD: Model Recommendation Through Graphical Dataset Representation
Authors: Noy Cohen-Shapira, Lior Rokach, Bracha Shapira, Gilad Katz, Roman Vainshtein
Venue: CIKM
Year: 2019

The widespread use of machine learning algorithms and the high level of expertise required to utilize them have fuelled the demand for solutions that can be used by non-experts. One of the main challenges non-experts face in applying machine learning to new problems is algorithm selection - the identification of the algorithm(s) that will deliver top performance for a given dataset, task, and evaluation measure. We present AutoGRD, a novel meta-learning approach for algorithm recommendation. AutoGRD first represents datasets as graphs and then extracts their latent representation that is used to train a ranking meta-model capable of accurately recommending top-performing algorithms for previously unseen datasets. We evaluate our approach on 250 datasets and demonstrate its effectiveness both for classification and regression tasks. AutoGRD outperforms state-of-the-art meta-learning and Bayesian methods.
Shared-Nothing Distributed Enumeration of 2-Plexes
Authors: Alessio Conte, Donatella Firmani, Maurizio Patrignani, Riccardo Torlone
Venue: CIKM
Year: 2019

We present a novel approach for the detection of 2-plexes, a popular relaxation of cliques used for modeling network communities. Specifically, with the purpose of identifying theoretically sound methods for community detection on a large scale, we introduce the first shared-nothing distributed algorithm for this problem. This result opens a new research direction for scalable community detection. Our proposal has three main ingredients: (i) we reduce the problem of finding 2-plexes to that of finding cliques; (ii) we leverage known algorithms for fast computation of cliques; (iii) we exploit a decomposition technique for a distributed shared-nothing computation. Preliminary experiments on a 10-nodes cluster running Spark confirm the effectiveness of our approach.
MedTruth: A Semi-supervised Approach to Discovering Knowledge Condition Information from Multi-Source Medical Data
Authors: Yang Deng, Yaliang Li, Ying Shen, Nan Du, Wei Fan, Min Yang, Kai Lei
Venue: CIKM
Year: 2019

Knowledge Graph (KG) contains entities and the relations between entities. Due to its representation ability, KG has been successfully applied to support many medical/healthcare tasks. However, in the medical domain, knowledge holds under certain conditions. Such conditions for medical knowledge are crucial for decision-making in various medical applications, which is missing in existing medical KGs. In this paper, we aim to discovery medical knowledge conditions from texts to enrich KGs. Electronic Medical Records (EMRs) are systematized collection of clinical data and contain detailed information about patients, thus EMRs can be a good resource to discover medical knowledge conditions. Unfortunately, the amount of available EMRs is limited due to reasons such as regularization. Meanwhile, a large amount of medical question answering (QA) data is available, which can greatly help the studied task. However, the quality of medical QA data is quite diverse, which may degrade the quality of the discovered medical knowledge conditions. In the light of these challenges, we propose a new truth discovery method, MedTruth, for medical knowledge condition discovery, which incorporates prior source quality information into the source reliability estimation procedure, and also utilizes the knowledge triple information for trustworthy information computation. We conduct series of experiments on real-world medical datasets to demonstrate that the proposed method can discover meaningful and accurate conditions for medical knowledge by leveraging both EMR and QA data. Further, the proposed method is tested on synthetic datasets to validate its effectiveness under various scenarios.
Adversarial Factorization Autoencoder for Look-alike Modeling
Authors: Khoa D. Doan, Pranjul Yadav, Chandan K. Reddy
Venue: CIKM
Year: 2019

Digital advertising is performed in multiple ways, for e.g., contextual, display-based and search-based advertising. Across these avenues, the primary goal of the advertiser is to maximize the return on investment. To realize this, the advertiser often aims to target the advertisements towards a targeted set of audience as this set has a high likelihood to respond positively towards the advertisements. One such form of tailored and personalized, targeted advertising is known as look-alike modeling, where the advertiser provides a set of seed users and expects the machine learning model to identify a new set of users such that the newly identified set is similar to the seed-set with respect to the online purchasing activity. Existing look-alike modeling techniques (i.e., similarity-based and regression-based) suffer from serious limitations due to the implicit constraints induced during modeling. In addition, the high-dimensional and sparse nature of the advertising data increases the complexity. To overcome these limitations, in this paper, we propose a novel Adversarial Factorization Autoencoder that can efficiently learn a binary mapping from sparse, high-dimensional data to a binary address space through the use of an adversarial training procedure. We demonstrate the effectiveness of our proposed approach on a dataset obtained from a real-world setting and also systematically compare the performance of our proposed approach with existing look-alike modeling baselines.
Forecasting Pavement Performance with a Feature Fusion LSTM-BPNN Model
Authors: Yushun Dong, Yingxia Shao, Xiaotong Li, Sili Li, Lei Quan, Wei Zhang, Junping Du
Venue: CIKM
Year: 2019

In modern pavement management systems, pavement roughness is an important indicator of pavement performance, and it reflects the smoothness of pavement surface. International Roughness Index (IRI) is the de-facto metric to quantitatively analyze the roughness of pavement surface. The pavement with high IRI not only reduces the lifetime of vehicles, but also raises the risk of car accidents. Accurate prediction of IRI becomes a key task for the pavement management system, and it helps the transportation department refurbish the pavement in time. However, existing models are proposed on top of small datasets, and have poor performance. Besides, they only consider cross-sectional features of the pavements without any time-series information. In order to better capture the latent relationship between the cross-sectional and time-series features, we propose a novel feature fusion LSTM-BPNN model. LSTM-BPNN first learns the cross-sectional and time-series features with two neural networks separately, then it fuses both features via an attention mechanism. Experimental results on a high-quality real-world dataset clearly demonstrate that the new model outperforms existing considerable alternatives.
Faster Algorithms for k-Regret Minimizing Sets via Monotonicity and Sampling
Authors: Qi Dong, Jiping Zheng
Venue: CIKM
Year: 2019

Regret-based queries are a complement of top-k and skyline queries when users cannot specify accurate utility functions while must output a controllable size of the query results. Various regret-based queries are proposed in last decade for multi-criteria decision making. The k-regret minimizing set (k-RMS) query which returns r points from the dataset and minimizes the maximum k-regret ratio has been extensively studied. However, existing state-of-art algorithms to find k-regret minimizing sets are very time-consuming and unapplicable. In this paper, we propose a faster algorithm SAMPGREED for k-RMS queries by utilizing the monotonicity of the regret ratio function with sampling techniques. We provide the theoretical analysis of our SAMPGREED algorithm and experiments on synthetic and real datasets verify our proposed algorithm is superior to existing state-of-art approaches.
MrMine: Multi-resolution Multi-network Embedding
Authors: Boxin Du, Hanghang Tong
Venue: CIKM
Year: 2019

Network embedding has become the cornerstone of a variety of mining tasks, such as classification, link prediction, clustering, anomaly detection and many more, thanks to its superior ability to encode the intrinsic network characteristics in a compact low-dimensional space. Most of the existing methods focus on a single network and/or a single resolution, which generate embeddings of different network objects (node/subgraph/network) from different networks separately. A fundamental limitation with such methods is that the intrinsic relationship across different networks (e.g., two networks share same or similar subgraphs) and that across different resolutions (e.g., the node-subgraph membership) are ignored, resulting in disparate embeddings. Consequentially, it leads to sub-optimal performance or even becomes inapplicable for some downstream mining tasks (e.g., role classification, network alignment. etc.). In this paper, we propose a unified framework MrMine to learn the representations of objects from multiple networks at three complementary resolutions (i.e., network, subgraph and node) simultaneously. The key idea is to construct the cross-resolution cross-network context for each object. The proposed method bears two distinctive features. First, it enables and/or boosts various multi-network downstream mining tasks by having embeddings at different resolutions from different networks in the same embedding space. Second, Our method is efficient and scalable, with a O(nlog(n)) time complexity for the base algorithm and a linear time complexity w.r.t. the number of nodes and edges of input networks for the accelerated version. Extensive experiments on real-world data show that our methods (1) are able to enable and enhance a variety of multi-network mining tasks, and (2) scale up to million-node networks.
Enterprise Knowledge Graph From Specific Business Task to Enterprise Knowledge Management
Authors: Rong Duan, Yanghua Xiao
Venue: CIKM
Year: 2019

Data driven Knowledge Graph is rapidly adapted by different societies. Many open domain and specific domain knowledge graphs have been constructed, and many industries have benefited from knowledge graph. Currently, enterprise related knowledge graph is classified as specific domain, but the applications span from solving a narrow specific problem to Enterprise Knowledge Management system. With the digital transform of traditional industry, Enterprise knowledge becomes more and more complicated, it involves knowledge from common domain, multiple specific domains, and corporate-specific in general. This tutorial provides an overview of current Enterprise Knowledge Graph(EKG). It distinguishes the EKG from specific domain according to the knowledge it covers, and provides the examples to illustrate the difference between EKG and specific domain KG. The tutorial further summarizes EKG into three types: Specific Business Task Enterprise KG, Specific Business Unit Enterprise KG and Cross Business Unit Enterprise KG, and illustrates the characteristics, steps, challenges, and future research in constructing and consuming of each of these three types of EKG .
Sketching Streaming Histogram Elements using Multiple Weighted Factors
Authors: Quang-Huy Duong, Heri Ramampiaro, Kjetil Nørvåg
Venue: CIKM
Year: 2019

We propose a novel sketching approach for streaming data that, even with limited computing resources, enables processing high volume and high velocity data efficiently. Our approach accounts for the fact that a stream of data is generally dynamic, with the underlying distribution possibly changing all the time. Specifically, we propose a hashing (sketching) technique that is able to automatically estimate a histogram from a stream of data by using a model with adaptive coefficients. Such a model is necessary to enable the preservation of histogram similarities, following the varying weight/importance of the generated histograms. To address the dynamic properties of data streams, we develop a novel algorithm that can sketch the histograms from a data stream using multiple weighted factors. The results from our extensive experiments on both synthetic and real-world datasets show the effectiveness and the efficiency of the proposed method.
Inclusion Dependency Discovery: An Experimental Evaluation of Thirteen Algorithms
Authors: Falco Dürsch, Axel Stebner, Fabian Windheuser, Maxi Fischer, Tim Friedrich, Nils Strelow, Tobias Bleifuß, Hazar Harmouch, Lan Jiang, Thorsten Papenbrock, Felix Naumann
Venue: CIKM
Year: 2019

Inclusion dependencies are an important type of metadata in relational databases, because they indicate foreign key relationships and serve a variety of data management tasks, such as data linkage, query optimization, and data integration. The discovery of inclusion dependencies is, therefore, a well-studied problem and has been addressed by many algorithms. Each of these discovery algorithms follows its own strategy with certain strengths and weaknesses, which makes it difficult for data scientists to choose the optimal algorithm for a given profiling task. This paper summarizes the different state-of-the-art discovery approaches and discusses their commonalities. For evaluation purposes, we carefully re-implemented the thirteen most popular discovery algorithms and discuss their individual properties. Our extensive evaluation on several real-world and synthetic datasets shows the unbiased performance of the different discovery approaches and, hence, provides a guideline on when and where each approach works best. Comparing the different runtimes and scalability graphs, we identify the best approaches for certain situations and demonstrate where certain algorithms fail.
ATENA: An Autonomous System for Data Exploration Based on Deep Reinforcement Learning
Authors: Ori Bar El, Tova Milo, Amit Somech
Venue: CIKM
Year: 2019

Exploratory Data Analysis (EDA), is an important yet challenging task, that requires profound analytical skills and familiarity with the data domain. While Deep Reinforcement Learning (DRL) is nowadays used to solve AI challenges previously considered to be intractable, to our knowledge such solutions have not yet been applied to EDA. In this work we present ATENA, an autonomous system capable of exploring a given dataset by executing a meaningfulsequence of EDA operations. ATENA uses a novel DRL architecture, and learns to perform EDA operations by independently interacting with the dataset, without any training data or human assistance. We demonstrate ATENA in the context ofcyber security log analysis, where the audience is invited to partake in a data exploration challenge: explore real-life network logs, assisted by ATENA, in order to reveal underlying security attacks hidden in the data.