OpenRaiser/Intern-Atlas huggingface dataset:
Deep Metric Learning Based on Rank-sensitive Optimization of Top-k Precision
Authors: Naoki Muramoto, Hai-Tao Yu
Venue: CIKM
Year: 2020

Deep metric learning has shown significantly increasing values in a wide range of domains, such as image retrieval, face recognition, zero-shot learning, to name a few. When evaluating the methods for deep metric learning, top-k precision is commonly used as a key metric, since few users bother to scroll down to lower-ranked items. Despite being widely studied, how to directly optimize top-k precision is still an open problem. In this paper, we propose a new method on how to optimize top-k precision in a rank-sensitive manner. Given the cutoff value k, our key idea is to impose different weights to further differentiate misplaced images sampled according to the top-k precision. To validate the effectiveness of the proposed method, we conduct a series of experiments on three widely used benchmark datasets. The experimental results demonstrate that: (1) Our proposed method outperforms the baseline methods on two datasets, which shows the potential value of rank-sensitive optimization of top-k precision for deep metric learning. (2) The factors, such as batch size and cutoff value k, significantly affect the performance of approaches that rely on optimising top-k precision for deep metric learning. Careful examinations of these factors are highly recommended.
Deep Generative Positive-Unlabeled Learning under Selection Bias
Authors: Byeonghu Na, Hyemi Kim, Kyungwoo Song, Weonyoung Joo, Yoon-Yeong Kim, Il-Chul Moon
Venue: CIKM
Year: 2020

Learning in the positive-unlabeled (PU) setting is prevalent in real world applications. Many previous works depend upon theSelected Completely At Random (SCAR) assumption to utilize unlabeled data, but the SCAR assumption is not often applicable to the real world due to selection bias in label observations. This paper is the first generative PU learning model without the SCAR assumption. Specifically, we derive the PU risk function without the SCAR assumption, and we generate a set of virtual PU examples to train the classifier. Although our PU risk function is more generalizable, the function requires PU instances that do not exist in the observations. Therefore, we introduce the VAE-PU, which is a variant of variational autoencoders to separate two latent variables that generate either features or observation indicators. The separated latent information enables the model to generate virtual PU instances. We test the VAE-PU on benchmark datasets with and without the SCAR assumption. The results indicate that the VAE-PU is superior when selection bias exists, and the VAE-PU is also competent under the SCAR assumption. The results also emphasize that the VAE-PU is effective when there are few positive-labeled instances due to modeling on selection bias.
AURORA: An Information Extraction System of Domain-specific Business Documents with Limited Data
Authors: Minh-Tien Nguyen, Le Tien Dung, Le Thai Linh, Nguyen Hong Son, Do Hoang Thai Duong, Bui Cong Minh, Nguyen Hai Phong, Nguyen Huu Hiep
Venue: CIKM
Year: 2020

Information extraction is a well-known topic that plays a critical role in many NLP applications as its outputs can be considered as an entrance step for digital transformation. However, there still exist gaps when applying research results to actual business cases. This paper introduces AURORA, an information extraction for domain-specific business documents. The intuition of AURORA is to use transfer learning for extraction. To do that, it utilizes the power of transformers for dealing with the limitation of training data in business cases and stacks additional layers for domain adaptation. We demonstrate AURORA in the context of actual scenarios where users are invited to experience two functions: fine-grained and whole paragraph extraction of Japanese business documents. A video of the system is available at http://y2u.be/xHQpYE41Tqw.
A Capsule Network-based Model for Learning Node Embeddings
Authors: Dai Quoc Nguyen, Tu Dinh Nguyen, Dat Quoc Nguyen, Dinh Phung
Venue: CIKM
Year: 2020

In this paper, we focus on learning low-dimensional embeddings for nodes in graph-structured data. To achieve this, we propose Caps2NE -- a new unsupervised embedding model leveraging a network of two capsule layers. Caps2NE induces a routing process to aggregate feature vectors of context neighbors of a given target node at the first capsule layer, then feed these features into the second capsule layer to infer a plausible embedding for the target node. Experimental results show that our proposed Caps2NE obtains state-of-the-art performances on benchmark datasets for the node classification task. Our code is available at: https://github.com/daiquocnguyen/Caps2NE.
Uncovering Semantic Bias in Neural Network Models Using a Knowledge Graph
Authors: Andriy Nikolov, Mathieu d&apos, Aquin
Venue: CIKM
Year: 2020

While neural networks models have shown impressive performance in many NLP tasks, lack of interpretability is often seen as a disadvantage. Individual relevance scores assigned by post-hoc explanation methods are not sufficient to show deeper systematic preferences and potential biases of the model that apply consistently across examples. In this paper we apply rule mining using knowledge graphs in combination with neural network explanation methods to uncover such systematic preferences of trained neural models and capture them in the form of conjunctive rules. We test our approach in the context of text classification tasks and show that such rules are able to explain a substantial part of the model behaviour as well as indicate potential causes of misclassifications when the model is applied outside of the initial training context.
Gated Heterogeneous Graph Representation Learning for Shop Search in E-commerce
Authors: Xichuan Niu, Bofang Li, Chenliang Li, Rong Xiao, Haochuan Sun, Honggang Wang, Hongbo Deng, Zhenzhong Chen
Venue: CIKM
Year: 2020

In e-commerce search, vectorized matching is the most important approach besides lexical matching, where learning vector representations for entities (e.g., query, item, shop) plays a crucial role. In this work, we focus on vectorized search matching model for shop search in Taobao. Unlike item search, shop search is faced with serious behavior sparsity and long-tail problem. To tackle this, we take the first step to transfer knowledge from item search, i.e., leveraging items purchased under a query and the shops they belong to. Moreover, we propose a novel gated heterogeneous graph learning model (named GHL) to derive vector representations for entities. Both first-order and second-order proximity of queries and shops are exploited to fully mine the heterogeneous relationships. And to relieve long-tail phenomenon, we devise an innovative gated neighbor aggregation scheme where each type of entities (i.e., hot ones and long-tail ones) can benefit from the heterogeneous graph in an automatic way. Finally, the whole framework is jointly trained in an end-to-end fashion. Offline evaluation results on real-world data of Taobao shop search platform demonstrate that the proposed model outperforms existing graph based methods, and online A/B tests show that it is highly effective and achieves significant CTR improvements.
STP-TrellisNets: Spatial-Temporal Parallel TrellisNets for Metro Station Passenger Flow Prediction
Authors: Junjie Ou, Jiahui Sun, Yichen Zhu, Haiming Jin, Yijuan Liu, Fan Zhang, Jianqiang Huang, Xinbing Wang
Venue: CIKM
Year: 2020

Recent years have witnessed a drastic increase in the number of urban metro passengers, which inevitably causes the overcrowdedness in the metro systems of many cities. Clearly, an accurate prediction of passenger flows at metro stations is critical for a variety of metro system management operations, such as line scheduling and staff preallocation, that help alleviate such overcrowdedness. Thus, in this paper, we aim to address the problem of accurately predicting metro station passenger (MSP) flows. Similar to other traffic data, such as road traffic volume and highway speed, MSP flows are also spatial-temporal in nature. However, existing methods for other traffic prediction tasks are usually suboptimal to predict MSP flows due to MSP flows' unique spatial-temporal characteristics. As a result, we propose a novel deep learning framework STP-TrellisNets, which for the first time augments the newly-emerged temporal convolutional framework TrellisNet for spatial-temporal prediction. The temporal module of STP-TrellisNets (named CP-TrellisNets) employs two TrellisNets in serial to jointly capture the short- and long-term temporal correlation of MSP flows. In parallel to CP-TrellisNets, its spatial module (named GC-TrellisNet) adopts a novel transfer flow-based metric to characterize the spatial correlation among MSP flows, and implements multiple diffusion graph convolutional networks (DGCNs) in time-series order with their outputs connected to a TrellisNet to capture the dynamics of such spatial correlation. Clearly, GC-TrellisNet essentially integrates TrellisNet with graph convolution, and empowers TrellisNet with the ability to capture dynamic graph-structured correlation. We conduct extensive experiments with two large-scale real-world automated fare collection datasets, which contain respectively about 1.5 billion records in Shenzhen, China and 70 million records in Hangzhou, China. The experimental results demonstrate that STP-TrellisNets outperforms the state-of-the-art baselines.
MiNet: Mixed Interest Network for Cross-Domain Click-Through Rate Prediction
Authors: Wentao Ouyang, Xiuwu Zhang, Lei Zhao, Jinmei Luo, Yu Zhang, Heng Zou, Zhaojie Liu, Yanlong Du
Venue: CIKM
Year: 2020

Click-through rate (CTR) prediction is a critical task in online advertising systems. Existing works mainly address the single-domain CTR prediction problem and model aspects such as feature interaction, user behavior history and contextual information. Nevertheless, ads are usually displayed with natural content, which offers an opportunity for cross-domain CTR prediction. In this paper, we address this problem and leverage auxiliary data from a source domain to improve the CTR prediction performance of a target domain. Our study is based on UC Toutiao (a news feed service integrated with the UC Browser App, serving hundreds of millions of users daily), where the source domain is the news and the target domain is the ad. In order to effectively leverage news data for predicting CTRs of ads, we propose the Mixed Interest Network (MiNet) which jointly models three types of user interest: 1) long-term interest across domains, 2) short-term interest from the source domain and 3) short-term interest in the target domain. MiNet contains two levels of attentions, where the item-level attention can adaptively distill useful information from clicked news / ads and the interest-level attention can adaptively fuse different interest representations. Offline experiments show that MiNet outperforms several state-of-the-art methods for CTR prediction. We have deployed MiNet in UC Toutiao and the A/B test results show that the online CTR is also improved substantially. MiNet now serves the main ad traffic in UC Toutiao.
DTMBIO 2020: The Fourteenth International Workshop on Data and Text Mining in Biomedical Informatics
Authors: Hyojung Paik, Sunyong Yoo, Hojung Nam, Mark Stevenson, Albert No
Venue: CIKM
Year: 2020

Over a decade, as a specialized workshop in the field of text mining applied to biomedical informatics, DTMBIO (ACM international workshop on Data and Text Mining in Biomedical Informatics) has been held annually in conjunction with one of the largest data management conferences, CIKM. The purpose of DTMBIO is to foster discussions regarding the state-of-the-art applications of data and text mining on biomedical research problems. To address our purpose, we bring together researchers working on computer science and bio/medical informatics area including text mining and high throughput genomic data analysis, such as the next generation Sequencing (NGS) data. DTMBIO 2020 will help scientists navigate emerging trends and opportunities in the evolving area of informatics related techniques and problems in the context of biomedical research.
Star Graph Neural Networks for Session-based Recommendation
Authors: Zhiqiang Pan, Fei Cai, Wanyu Chen, Honghui Chen, Maarten de Rijke
Venue: CIKM
Year: 2020

Session-based recommendation is a challenging task. Without access to a user's historical user-item interactions, the information available in an ongoing session may be very limited. Previous work on session-based recommendation has considered sequences of items that users have interacted with sequentially. Such item sequences may not fully capture complex transition relationship between items that go beyond inspection order. Thus graph neural network (GNN) based models have been proposed to capture the transition relationship between items. However, GNNs typically propagate information from adjacent items only, thus neglecting information from items without direct connections. Importantly, GNN-based approaches often face serious overfitting problems. We propose Star Graph Neural Networks with Highway Networks (SGNN-HN) for session-based recommendation. The proposed SGNN-HN applies a star graph neural network (SGNN) to model the complex transition relationship between items in an ongoing session. To avoid overfitting, we employ highway networks (HN) to adaptively select embeddings from item representations. Finally, we aggregate the item embeddings generated by the SGNN in an ongoing session to represent a user's final preference for item prediction. Experiments on two public benchmark datasets show that SGNN-HN can outperform state-of-the-art models in terms of P@20 and MRR@20 for session-based recommendation.
Efficient Detection of Data Dependency Violations
Authors: Eduardo H. M. Pena, Edson Ramiro Lucas Filho, Eduardo C. de Almeida, Felix Naumann
Venue: CIKM
Year: 2020

Research on data dependencies has experienced a revival as dependency violations can reveal errors in data. Several data cleaning systems use a DBMS to detect such violations. While DBMSs are efficient for some kinds of data dependencies (e.g., unique constraints), they are likely to fall short of satisfactory performance for more complex ones, such as order dependencies. We present a novel system to efficiently detect violations of denial constraints (DCs), a well-known formalism that generalizes many kinds of data dependencies. We describe its execution model, which operates on a compressed block of tuples at-a-time, and we present various algorithms that take advantage of the predicate form in the DCs to provide effective code patterns. Our experimental evaluation includes comparisons with DBMS-based and DC-specific approaches, real-world and synthetic data, and various kinds of DCs. It shows that our system is up to three orders-of-magnitude faster than the other solutions, especially for datasets with a large number of tuples and DCs that identify a large number of violations.
Learning to Infer User Hidden States for Online Sequential Advertising
Authors: Zhaoqing Peng, Junqi Jin, Lan Luo, Yaodong Yang, Rui Luo, Jun Wang, Weinan Zhang, Haiyang Xu, Miao Xu, Chuan Yu, Tiejian Luo, Han Li, Jian Xu, Kun Gai
Venue: CIKM
Year: 2020

To drive purchase in online advertising, it is of the advertiser's great interest to optimize the sequential advertising strategy whose performance and interpretability are both important. The lack of interpretability in existing deep reinforcement learning methods makes it not easy to understand, diagnose and further optimize the strategy.In this paper, we propose our Deep Intents Sequential Advertising (DISA) method to address these issues. The key part of interpretability is to understand a consumer's purchase intent which is, however, unobservable (called hidden states). In this paper, we model this intention as a latent variable and formulate the problem as a Partially Observable Markov Decision Process (POMDP) where the underlying intents are inferred based on the observable behaviors. Large-scale industrial offline and online experiments demonstrate our method's superior performance over several baselines. The inferred hidden states are analyzed, and the results prove the rationality of our inference.
Vallum-Med: Protecting Medical Data in Cloud Environments
Authors: Ronny Peterson, Altigran S. da Silva, André Carvalho, Christof Fetzer, André Martin, Ignacio Blanquer
Venue: CIKM
Year: 2020

Despite the many advantages of cloud computing, keeping information in such an environment increases the risk of cyber attacks, as well as the possibility of unauthorized access by cloud provider employees. Another critical concern is privacy protection, since depending on data access control, confidential information may be exposed even through authorized access. To solve these issues we have previously proposed Vallum, a platform that leverages Intel SGX protection to ensure the security, confidentiality, and integrity of data at rest and during processing. It also provides tools for privacy protection, following policies set by the data owner. In this demo we present Vallum-Med, an application of Vallum for the protection of medical patient personal data, including imaging results of their cardiac examinations. We will demonstrate that this system fully supports cloud protection of such sensitive data as well as the definition of privacy policies and ensuring that all results of queries are compliant to these policies. All processing, data storage and network traffic are protected using SCONE, a docker container-based technology for seamlessly incorporating SGX protection for applications, which provides a fully encrypted memory environment.
Search-based User Interest Modeling with Lifelong Sequential Behavior Data for Click-Through Rate Prediction
Authors: Qi Pi, Guorui Zhou, Yujing Zhang, Zhe Wang, Lejian Ren, Ying Fan, Xiaoqiang Zhu, Kun Gai
Venue: CIKM
Year: 2020

Rich user behavior data has been proven to be of great value for click-through rate prediction tasks, especially in industrial applications such as recommender systems and online advertising. Both industry and academy have paid much attention to this topic and propose different approaches to modeling with long sequential user behavior data. Among them, memory network based model MIMN proposed by Alibaba, achieves SOTA with the co-design of both learning algorithm and serving system. MIMN is the first industrial solution that can model sequential user behavior data with length scaling up to 1000. However, MIMN fails to precisely capture user interests given a specific candidate item when the length of user behavior sequence increases further, say, by 10 times or more. This challenge exists widely in previously proposed approaches. In this paper, we tackle this problem by designing a new modeling paradigm, which we name as Search-based Interest Model (SIM). SIM extracts user interests with two cascaded search units: (i) General Search Unit (GSU) acts as a general search from the raw and arbitrary long sequential behavior data, with query information from candidate item, and gets a Sub user Behavior Sequence (SBS) which is relevant to candidate item; (ii) Exact Search Unit (ESU) models the precise relationship between candidate item and SBS. This cascaded search paradigm enables SIM with a better ability to model lifelong sequential behavior data in both scalability and accuracy. Apart from the learning algorithm, we also introduce our hands-on experience on how to implement SIM in large scale industrial systems. Since 2019, SIM has been deployed in the display advertising system in Alibaba, bringing 7.1% CTR and 4.4% RPM lift, which is significant to the business. Serving the main traffic in our real system now, SIM models sequential user behavior data with maximum length reaching up to 54000, pushing SOTA to 54x.
Challenges and Solutions to the Student Dropout Prediction Problem in Online Courses
Authors: Bardh Prenkaj, Giovanni Stilo, Lorenzo Madeddu
Venue: CIKM
Year: 2020

Online courses and e-degrees, although present since the mid-1990, have received enormous attention only in the last decade. Moreover, the new Coronavirus disease (COVID-19) outbreak forced many nations (e.g. Italy, the US, and other countries) to massively push their education system towards an online environment. Academics now are also looking at the crisis as an opportunity for universities to adopt digital technologies for teaching more broadly. But they will have to understand what possible ways of evaluating and effectively teaching will be in this new scenario. The depicted overview, in conjunction with the utility and ubiquitous access to the educational platforms of online courses, entails a vast amount of enrolments. Nevertheless, a high enrolment rate usually translates into a significant dropout (or withdrawal) rate of students (40-80% of online students drop out). Student dropout prediction (SDP) consists of modelling and fore-casting student behaviour when interacting with e-learning platforms. It is a significant phenomenon that has repercussions on online institutions, the involved students and professors. Early approaches tended to perform manual analytic examinations to devise retention strategies. Recent research has adopted automated policies to thoroughly exploit the advantages of student activities(hereafter e-tivities) in the e-platforms and identify at-risk students. These approaches include machine learning and deep learning techniques to predict the student dropout status. Therefore, being able to cope with the trend shifting of student interactions with the course platforms in real-time has become of paramount importance. In this tutorial, we comprehensively overview the SDP problem in the literature. We provide mathematical formalisation to the different definitions proposed, and we introduce simple and complex predictive methods adhering to the following: Student dropout definition, Input modelling, Underlying machine and deep learning techniques, Evaluation measures, Datasets, and privacy concerns.
EnDeA: Ensemble based Decoupled Adversarial Learning for Identifying Infrastructure Damage during Disasters
Authors: Shalini Priya, Apoorva Upadhyaya, Manish Bhanu, Sourav Kumar Dandapat, Joydeep Chandra
Venue: CIKM
Year: 2020

Identifying tweets related to infrastructure damage during a crisis event is an important problem. However, the unavailability of labeled data during the early stages of a crisis event poses major challenge in training suitable models. Several domain adaptation strategies have been proposed for text classification that can be used to train models using available source data of previous crisis events and apply on a target data related to a current event. However, these approaches are insufficient to handle the distribution drift in the source and target data along with the class imbalance in the target data. In this paper we introduce an Ensemble learning approach with a Decoupled Adversarial (EnDeA) model to classify infrastructure damage tweets in a target tweet dataset. EnDeA is an ensemble of three different models two of which separately learn the event invariant and specific features of a target data from a set of source and target data. The third model which is an adversarial model helps to improve the prediction accuracy of both models. Unlike the existing approaches that also identify the domain invariant and specific properties of target data for sentiment classification, our method works for short texts and can better handle the distribution drift and class imbalance problem. We rigorously investigate the performance of the proposed approach using multiple public datasets and compare it with several state-of-the-art baselines. We discover that EnDeA outperforms these baselines with around 20% improvement in the 1 scores.