OpenRaiser/Intern-Atlas huggingface dataset:
DATSING: Data Augmented Time Series Forecasting with Adversarial Domain Adaptation
Authors: Hailin Hu, MingJian Tang, Chengcheng Bai
Venue: CIKM
Year: 2020

Due to the high temporal uncertainty and low signal-to-noise ratio, transfer learning for univariate time series forecasting remains a challenging task. In addition, data scarcity, which is commonly encountered in business forecasting, further limits the application of conventional transfer learning protocols. In this work, we have developed, DATSING, a transfer learning-based framework that effectively leverages cross-domain time series latent representations to augment target domain forecasting. In particular, we aim to transfer domain-invariant feature representations from a pre-trained stacked deep residual network to the target domains, so as to assist the prediction of each target time series. To effectively avoid noisy feature representations, we propose a two-phased framework which first clusters similar mixed domains time series data and then performs a fine-tuning procedure with domain adversarial regularization to achieve better out-of-sample generalization. Extensive experiments with real-world datasets have demonstrated that our method significantly improves the forecasting performance of the pre-trained model. DATSING has the unique potential to empower forecasting practitioners to unleash the power of cross-domain time series data.
Loan Default Analysis with Multiplex Graph Learning
Authors: Binbin Hu, Zhiqiang Zhang, Jun Zhou, Jingli Fang, Quanhui Jia, Yanming Fang, Quan Yu, Yuan Qi
Venue: CIKM
Year: 2020

Aiming to effectively distinguish loan default in the Mobile Credit Payment Service, industrial efforts mainly attempt to employ conventional classifier with complicated feature engineer for prediction. However, these solutions fail to exploit multiplex relations existed in the financial scenarios and ignore the key intrinsic properties of the loan default detection, i.e., communicability, complementation and induction. To address these issues, we develop a novel attributed multiplex graph based loan default detection approach for effectively integrating multiplex relations in financial scenarios. Considering the complexity of financial scenario, an Attributed Multiplex Graph (AMG) is proposed to jointly model various relations and objects as well as the rich attributes on nodes and edges. We elaborately design relation-specific receptive layers equipped with adaptive breadth function to incorporate important information derived from local structure in each aspect of AMG and stack multiple propagation layer to explore the high-order connectivity information. Furthermore, a relation-specific attention mechanism is adopted to emphasize relevant information during end-to-end training. Extensive experiments conducted on the large-scale real- world dataset verify the effectiveness of the proposed model com- pared with state of arts. Moreover, AMG-DP has also achieved a performance improvement of 9.37% on KS metric in recent months after successful deployment in the Alipay APP.
Approximate Event Pattern Matching over Heterogeneous and Dirty Sources
Authors: Ruihong Huang
Venue: CIKM
Year: 2020

Pattern matching is an important task in the field of Complex Event Processing (CEP). However, exact event pattern matching methods could suffer from low hit rate and loss for meaningful events identification due to the heterogeneous and dirty sources in the big data era. Since both events and patterns could be imprecise, the actual event trace may have different event names as well as structures from the pre-defined pattern. The low-quality data even intensifies the difficulty of matching. In this work, we propose to learn embedding representations for patterns and event traces separately and calculate their similarity as the scores for approximate matching.
Image Captioning with Internal and External Knowledge
Authors: Feicheng Huang, Zhixin Li, Shengjia Chen, Canlong Zhang, Huifang Ma
Venue: CIKM
Year: 2020

Automatically generating a human-like description for a given image is a potential research in artificial intelligence, which has attracted a great of attention recently. Most of the existing attention methods explore the mapping relationships between words in sentence and regions in image, such unpredictable matching manner sometimes causes inharmonious alignments that may reduce the quality of generated captions. In this paper, we make our efforts to reason about more accurate and meaningful captions. We first propose word attention to improve the correctness of visual attention when generating sequential descriptions word-by-word. The special word attention emphasizes on word importance when focusing on different regions of the input image, and makes full use of the internal annotation knowledge to assist the calculation of visual attention. Then, in order to reveal those incomprehensible intentions that cannot be expressed straightforwardly by machines, we inject external knowledge extracted from knowledge graph into the encoder-decoder framework to facilitate meaningful captioning. We validate our model on two freely available captioning benchmarks: Microsoft COCO dataset and Flickr30k dataset. The results demonstrate that our approach achieves state-of-the-art performance and outperforms many of the existing approaches.
GNNVis: Visualize Large-Scale Data by Learning a Graph Neural Network Representation
Authors: Yajun Huang, Jingbin Zhang, Yiyang Yang, Zhiguo Gong, Zhifeng Hao
Venue: CIKM
Year: 2020

Many achievements have been made by studying how to visualize large-scale and high-dimensional data in typically 2D or 3D space. Normally, such a process is performed through a non-parametric (unsupervised) approach which is limited in handling the unseen data. In this work, we study the parametric (supervised) model which is capable to learn a mapping between high-dimensional data space Rd and low-dimensional latent space Rs with similarity structure in Rd preserved where s l d. The GNNVis is proposed, a framework that applies the idea of Graph Neural Networks (GNNs) to the parametric learning process and the learned mapping serves as a Visualizer (Vis) to compute the low-dimensional embeddings of unseen data online. In our framework, the features of data nodes, as well as the (hidden) information of their neighbors are fused to conduct Dimension Reduction. To the best of our knowledge, none of the existing visualization works have studied how to combine such information into the learning representation. Moreover, the learning process of GNNVis is designed as an end-to-end manner and can easily be extended to arbitrary Dimension Reduction methods if the corresponding objective function is given. Based on GNNVis, several typical dimension reduction methods t-SNE, LargeVis, and UMAP are investigated. As a parametric framework, GNNVis is an inherently efficient Visualizer capable of computing the embeddings of large-scale unseen data. To guarantee its scalability in the Training Stage, a novel training strategy with Subgraph Negative Sampling (SNS) is conducted to reduce the corresponding cost. Experimental results in real datasets demonstrate the advantages of GNNVis. The visualization quality of GNNVis outperforms the state-of-the-art parametric models, and is comparable to that of the non-parametric models.
Homogenization with Explicit Semantics Preservation for Heterogeneous Information Network
Authors: Tiancheng Huang, Zifeng Zhuang, Shanshan Zhang, Donglin Wang
Venue: CIKM
Year: 2020

Heterogeneous information network (HIN), especially its embedding task, has drawn much attention recently as its rich latent information brings great benefits to complex classification and clustering. Many prior embedding works focus on designing a specific approach for the HIN while others implicitly homogenize the HIN with losing some semantic information. In this paper, a novel explicit homogenization method is proposed to preserve more semantic information, where the latent information of intermediate nodes among each meta-path instance and that among multiple meta-path instances are incorporated into the conventional adjacent matrix (or weight matrix). Then, the transfer of weight matrix and the fusion of node-level embeddings are considered to obtain graph-level embedding to solve the HIN problem. In such way, much more latent information of meta-path is preserved so that the proposed method exhibits its superiority in comparison to state-of-the-art works in classification and clustering tasks.
Predicting Economic Growth by Region Embedding: A Multigraph Convolutional Network Approach
Authors: Bo Hui, Da Yan, Wei-Shinn Ku, Wenlu Wang
Venue: CIKM
Year: 2020

With the rapid progress of global urbanization and function division among different geographical regions, it is of urgent need to develop methods that can find regions of desired future function distributions in applications. For example, a company tends to open a new branch in a region where the growth trend of industrial sectors fits its strategic goals, or is similar to that of an existing company location; while a job hunter tends to search regions where his/her expertise aligns with the industrial growth trend providing sufficient job opportunities to sustain future employment and job-hopping. Our solution is to learn a distribution (aka. embedding) of the growth of various industrial sectors for each region, so that the embeddings of different regions can be searched, or compared for similarity querying. We consider the fine granularity of ZIP code areas as they are usually representative of the regional functions. By effectively utilizing open data on the Internet such as government data (e.g., from US Census Bureau) and third-party data for supervised learning, we propose to first construct a multigraph that captures the various relationships between regions such as direct flight connections and shared school districts, and then learn region embeddings using a novel graph convolutional network architecture. Our multigraph convnet (MGCN) differentiates various feature types such as demographic, social, economic and housing features, and learns different weights on different features and spatial relationships for effective data-driven feature aggregation. While deep learning is known to require large amounts of data to train, our weighted MGCN (WMGCN) is designed to minimize the number of parameters so that it does not underfit on the limited amount of open data. Extensive experiments are conducted to compare our WMGCN model with several competitive baselines to demonstrate the superiority of our WMGCN design.
SDM-RDFizer: An RML Interpreter for the Efficient Creation of RDF Knowledge Graphs
Authors: Enrique Iglesias, Samaneh Jozashoori, David Chaves-Fraga, Diego Collarana, Maria-Esther Vidal
Venue: CIKM
Year: 2020

In recent years, the amount of data has increased exponentially, and knowledge graphs have gained attention as data structures to integrate data and knowledge harvested from myriad data sources. However, data complexity issues like large volume, high-duplicate rate, and heterogeneity usually characterize these data sources, being required data management tools able to address the negative impact of these issues on the knowledge graph creation process. In this paper, we propose the SDM-RDFizer, an interpreter of the RDF Mapping Language (RML), to transform raw data in various formats into an RDF knowledge graph. SDM-RDFizer implements novel algorithms to execute the logical operators between mappings in RML, allowing thus to scale up to complex scenarios where data is not only broad but has a high-duplication rate. We empirically evaluate the SDM-RDFizer performance against diverse testbeds with diverse configurations of data volume, duplicates, and heterogeneity. The observed results indicate that SDM-RDFizer is two orders of magnitude faster than state of the art, thus, meaning that SDM-RDFizer an interoperable and scalable solution for knowledge graph creation. SDM-RDFizer is publicly available as a resource through a Github repository and a DOI.
Sequential Recommender via Time-aware Attentive Memory Network
Authors: Wendi Ji, Keqiang Wang, Xiaoling Wang, Tingwei Chen, Alexandra I. Cristea
Venue: CIKM
Year: 2020

Recommendation systems aim to assist users to discover most preferred contents from an ever-growing corpus of items. Although recommenders have been greatly improved by deep learning, they still face several challenges: (1) Behaviors are much more com- plex than words in sentences, so traditional attentive and recurrent models have limitations capturing the temporal dynamics of user preferences. (2) The preferences of users are multiple and evolving, so it is difficult to integrate long-term memory and short-term intent. In this paper, we propose a temporal gating methodology to improve attention mechanism and recurrent units, so that temporal information can be considered in both information filtering and state transition. Additionally, we propose a hybrid sequential recommender, named Multi-hop Time-aware Attentive Memory network (MTAM), to integrate long-term and short-term preferences. We use the proposed time-aware GRU network to learn the short-term intent and maintain prior records in user memory. We treat the short-term intent as a query and design a multi-hop memory reading operation via the proposed time-aware attention to generate user representation based on the current intent and long-term memory. Our approach is scalable for candidate retrieval tasks and can be viewed as a non-linear generalization of latent factorization for dot-product based Top-K recommendation. Finally, we conduct extensive experiments on six benchmark datasets and the experimental results demonstrate the effectiveness of our MTAM and temporal gating methodology.
MARU: Meta-context Aware Random Walks for Heterogeneous Network Representation Learning
Authors: Jyun-Yu Jiang, Zeyu Li, Chelsea J.-T. Ju, Wei Wang
Venue: CIKM
Year: 2020

Information networks, such as social and citation networks, are ubiquitous in the real world so that network analysis plays an important role in data mining and knowledge discovery. To alleviate the sparsity problem of network analysis, it is common to capture the network semantics by projecting nodes onto a vector space as network embeddings. Moreover, random walks are usually exploited to efficiently learn node embeddings and preserve network proximity. In addition to proximity structure, heterogeneous networks have more knowledge about the types of nodes. However, to profit from heterogeneous knowledge, most of the existing approaches guide the random walks through predefined meta-paths or specific strategies, which can distort the understanding of network structures. Furthermore, traditional random walk-based approaches much favor the nodes with higher degrees while other nodes are equivalently important for the downstream applications. In this paper, we propose Meta-context Aware Random Walks (MARU) to overcome these challenges, thereby learning richer and more unbiased representations for heterogeneous networks. To reduce the bias in classical random walks, the algorithm of bidirectional extended random walks is introduced to improve the fairness of representation learning. Based on the enhanced random walks, the meta-context aware skip-gram model is then presented to learn robust network embeddings with dynamic meta-contexts. Therefore, MARU can not only fairly understand the overall network structures but also leverage the sophisticated heterogeneous knowledge in the networks. Extensive experiments have been conducted on three real-world large-scale publicly available datasets. The experimental results demonstrate that MARU significantly outperforms state-of-the-art heterogeneous network embedding methods across three general machine learning tasks, including multi-label node classification, node clustering, and link prediction.
Social Factors in Closed-Network Content Consumption
Authors: Parisa Kaghazgaran, Maarten W. Bos, Leonardo Neves, Neil Shah
Venue: CIKM
Year: 2020

How do users on social platforms consume content shared by their friends? Is this consumption socially motivated, and can we predict it? Considerable prior work has focused on inferring and learning user preferences with respect to broadcasted, or open-network content in public spheres like webpages or public videos. However, user engagement with narrowcasted, closed-network content shared by their friends is considerably under-explored, despite being a commonplace activity. Here we bridge this gap by focusing on consumption of visual media content in closed-network settings, using data from Snapchat, a large multimedia-driven social sharing service with over 200M daily active users. Broadly, we answer questions around content consumption patterns, social factors that are associated with such consumption habits, and predictability of consumption time. We propose models for patterns in users' time-spending behaviors across friends, and observe that viewers preferentially and consistently spend more time on content from certain friends, even without considering any explicit notion of intrinsic content value. We also find that consumption time is highly correlated with several engagement-based social factors, suggesting a large social role in closed-network content consumption. Finally, we propose a novel approach of modeling future consumption time as a learning-to-rank task over users? friends. Our results demonstrate significant predictive value (0.815 P@1, 0.650 nDCG@10) using only social factors. We expect our work to motivate additional research in modeling consumption and ranking of online closed-network content.
CR-Graph: Community Reinforcement for Accurate Community Detection
Authors: Yoonsuk Kang, Jun Seok Lee, Won-Yong Shin, Sang-Wook Kim
Venue: CIKM
Year: 2020

In this paper, we present CR-Graph (community reinforcement on graphs), a novel method that helps existing algorithms to perform more-accurate community detection (CD). Toward this end, CR-Graph strengthens the community structure of a given original graph by adding non-existent predicted intra-community edges and deleting existing predicted inter-community edges. To design CR-Graph, we propose the following two strategies: (1) predicting intra-community and inter-community edges (i.e., the type of edges) and (2) determining the amount of edges to be added/deleted. To show the effectiveness of CR-Graph, we conduct extensive experiments with various CD algorithms on 7 synthetic and 4 real-world graphs. The results demonstrate that CR-Graph improves the accuracy of all underlying CD algorithms universally and consistently.
Multimodal Knowledge Graph for Deep Learning Papers and Code
Authors: Amar Viswanathan Kannan, Dmitriy Fradkin, Ioannis Akrotirianakis, Tugba Kulahcioglu, Arquimedes Canedo, Aditi Roy, Shih-Yuan Yu, Arnav V. Malawade, Mohammad Abdullah Al Faruque
Venue: CIKM
Year: 2020

Keeping up with the rapid growth of Deep Learning (DL) research is a daunting task. While existing scientific literature search systems provide text search capabilities and can identify similar papers, gaining an in-depth understanding of a new approach or an application is much more complicated. Many publications leverage multiple modalities to convey their findings and spread their ideas - they include pseudocode, tables, images and diagrams in addition to text, and often make publicly accessible their implementations. It is important to be able to represent and query them as well. We utilize RDF Knowledge graphs (KGs) to represent multimodal information and enable expressive querying over modalities. In our demo we present an approach for extracting KGs from different modalities, namely text, architecture images and source code. We show how graph queries can be used to get insights into different facets (modalities) of a paper, and its associated code implementation. Our innovation lies in the multimodal nature of the KG we create. While our work is of direct interest to DL researchers and practitioners, our approaches can also be leveraged in other scientific domains.
Empirical Analysis of Impact of Query-Specific Customization of nDCG: A Case-Study with Learning-to-Rank Methods
Authors: Shubhra (Santu) K. Karmaker, Parikshit Sondhi, ChengXiang Zhai
Venue: CIKM
Year: 2020

In most existing works, nDCG is computed for a fixed cutoff k, i.e., nDCG@k and some fixed discounting coefficient. Such a conventional query-independent way to compute nDCG does not accurately reflect the utility of search results perceived by an individual user and is thus non-optimal. In this paper, we conduct a case study of the impact of using query-specific nDCG on the choice of the optimal Learning-to-Rank (LETOR) methods, particularly to see whether using a query-specific nDCG would lead to a different conclusion about the relative performance of multiple LETOR methods than using the conventional query-independent nDCG would otherwise. Our initial results show that the relative ranking of LETOR methods using query-specific nDCG can be dramatically different from those using the query-independent nDCG at the individual query level, suggesting that query-specific nDCG may be useful in order to obtain more reliable conclusions in retrieval experiments.
What Rankers Can be Statistically Distinguished in Multileaved Comparisons?
Authors: Makoto P. Kato, Akiomi Nishida, Tomohiro Manabe, Sumio Fujita, Takehiro Yamamoto
Venue: CIKM
Year: 2020

This paper presents findings from an empirical study of multileaved comparisons, an efficient online evaluation methodology, in a commercial Web service. The most important difference from the previous studies is the number of rankers involved in the online evaluation: we compared 30 rankers for around 90 days by multileaved comparisons. A relatively large number of rankers answered several questions that could not be addressed in the previous work due to a small number of rankers: How much ranking difference is required for rankers to be statistically distinguished? How many impressions are necessary for finding statistically significant differences for correlated rankers? How large difference in offline evaluation can predict significant differences in a multileaved comparison? We answer these questions with the results of the multileaved comparisons, and generalized some of the findings by simulation-based experiments.
Incorporating User Feedback into Sequence to Sequence Model Training
Authors: Michaeel Kazi, Weiwei Guo, Huiji Gao, Bo Long
Venue: CIKM
Year: 2020

As the largest professional network, LinkedIn hosts millions of user profiles and job postings. Users effectively find what they need by entering search queries. However, finding what they are looking for can be a challenge, especially if they are unfamiliar with specific keywords from their industry. Query Suggestion is a popular feature where a search engine can suggest alternate, related queries. At LinkedIn, we have productionized a deep learning Seq2Seq model to transform an input query into several alternatives. This model is trained by examining search history directly typed by users. Once online, we can determine whether or not users clicked on suggested queries. This new feedback data indicates which suggestions caught the user's attention. In this work, we propose training a model with both the search history and user feedback datasets. We examine several ways to incorporate feedback without any architectural change, including adding a novel pairwise ranking loss term during training. The proposed new training technique produces the best combined score out of several alternatives in offline metrics. Deployed in the LinkedIn search engine, it significantly outperforms the control model with respect to key business metrics.