OpenRaiser/Intern-Atlas huggingface dataset:
The Difference Between a Click and a Cart-Add: Learning Interaction-Specific Embeddings
Authors: Xiaoting Zhao, Raphael Louca, Diane Hu, Liangjie Hong
Venue: WWW
Year: 2020

For large-scale online marketplaces with over millions of items, users come to rely on personalized recommendations to find relevant items from their massive inventory. One hallmark of the shopping experience in such online marketplaces is the many ways a user can interact with an item they are interested in: they can click it, favorite it, add it to cart, purchase it, etc. We hypothesize that the different ways in which a user interacts with an item indicates different kinds of intent. Consequently, a user’s recommendations should be based not only on items from their past activity, but also the way in which they interacted with these items. Co-occurrence based methods have been successfully used to give recommendations that incorporate interaction types, such as the popular “Because you purchased X, you may also purchase Y”. In this paper, we propose a novel method that generalizes upon the co-occurrence methods to learn interaction-based item embeddings that encode the co-occurrence patterns of not only the item itself, but also the interaction type. The learned embeddings provide a convenient way of approximating the likelihood that one item-interaction pair would co-occur with another by way of a simple inner product. To show their effectiveness, we deploy the interaction-based embeddings in an industry-scale recommendation system that serves live traffic on Etsy.com. We find that taking interaction types into account shows significant improvements in accurately modeling user shopping behavior for both online and offline metrics.
Multiple Knowledge Syncretic Transformer for Natural Dialogue Generation
Authors: Xiangyu Zhao, Longbiao Wang, Ruifang He, Ting Yang, Jinxin Chang, Ruifang Wang
Venue: WWW
Year: 2020

Knowledge is essential for intelligent conversation systems to generate informative responses. This knowledge comprises a wide range of diverse modalities such as knowledge graphs (KGs), grounding documents and conversation topics. However, limited abilities in understanding language and utilizing different types of knowledge still challenge existing approaches. Some researchers try to enhance models’ language comprehension ability by employing the pre-trained language models, but they neglect the importance of external knowledge in specific tasks. In this paper, we propose a novel universal transformer-based architecture for dialogue system, the Multiple Knowledge Syncretic Transformer (MKST), which fuses multi-knowledge in open-domain conversation. Firstly, the model is pre-trained on a large-scale corpus to learn commonsense knowledge. Then during fine-tuning, we divide the type of knowledge into two specific categories that are handled in different ways by our model. While the encoder is responsible for encoding dialogue contexts with multifarious knowledge together, the decoder with a knowledge-aware mechanism attentively reads the fusion of multi-knowledge to promote better generation. This is the first attempt that fuses multi-knowledge in one conversation model. The experimental results have been demonstrated that our model achieves significant improvement on knowledge-driven dialogue generation tasks than state-of-the-art baselines. Meanwhile, our new benchmark could facilitate the further study in this research area.
Condition Aware and Revise Transformer for Question Answering
Authors: Xinyan Zhao, Feng Xiao, Haoming Zhong, Jun Yao, Huanhuan Chen
Venue: WWW
Year: 2020

The study of question answering has received increasing attention in recent years. This work focuses on providing an answer that compatible with both user intent and conditioning information corresponding to the question, such as delivery status and stock information in e-commerce. However, these conditions may be wrong or incomplete in real-world applications. Although existing question answering systems have considered the external information, such as categorical attributes and triples in knowledge base, they all assume that the external information is correct and complete. To alleviate the effect of defective condition values, this paper proposes condition aware and revise Transformer (CAR-Transformer). CAR-Transformer (1) revises each condition value based on the whole conversation and original conditions values, and (2) it encodes the revised conditions and utilizes the conditions embedding to select an answer. Experimental results on a real-world customer service dataset demonstrate that the CAR-Transformer can still select an appropriate reply when conditions corresponding to the question exist wrong or missing values, and substantially outperforms baseline models on automatic and human evaluations. The proposed CAR-Transformer can be extended to other NLP tasks which need to consider conditioning information.
Addressing the Target Customer Distortion Problem in Recommender Systems
Authors: Xing Zhao, Ziwei Zhu, Majid Alfifi, James Caverlee
Venue: WWW
Year: 2020

Predicting the potential target customers for a product is essential. However, traditional recommender systems typically aim to optimize an engagement metric without considering the overall distribution of target customers, thereby leading to serious distortion problems. In this paper, we conduct a data-driven study to reveal several distortions that arise from conventional recommenders. Toward overcoming these issues, we propose a target customer re-ranking algorithm to adjust the population distribution and composition in the Top-k target customers of an item while maintaining recommendation quality. By applying this proposed algorithm onto a real-world dataset, we find the proposed method can effectively make the class distribution of items’ target customers close to the desired distribution, thereby mitigating distortion.
Learning to Classify: A Flow-Based Relation Network for Encrypted Traffic Classification
Authors: Wenbo Zheng, Chao Gou, Lan Yan, Shaocong Mo
Venue: WWW
Year: 2020

As the size and source of network traffic increase, so does the challenge of monitoring and analyzing network traffic. The challenging problems of classifying encrypted traffic are the imbalanced property of network data, the generalization on an unseen dataset, and overly dependent on data size. In this paper, we propose an application of a meta-learning approach to address these problems in encrypted traffic classification, named Flow-Based Relation Network (RBRN). The RBRN is an end-to-end classification model that learns representative features from the raw flows and then classifies them in a unified framework. Moreover, we design “hallucinator” to produce additional training samples for the imbalanced classification, and then focus on meta-learning to classify unseen categories from few labeled samples. We validate the effectiveness of the RBRN on the real-world network traffic dataset, and the experimental results demonstrate that the RBRN can achieve an excellent classification performance and outperform the state-of-the-art methods on encrypted traffic classification. What is more interesting, our model trained on the real-world dataset can generalize very well to unseen datasets, outperforming multiple state-of-art methods.
Learning Graph Neural Networks with Deep Graph Library
Authors: Da Zheng, Minjie Wang, Quan Gan, Zheng Zhang, George Karypis
Venue: WWW
Year: 2020

Learning from graph and relational data plays a major role in many applications including social network analysis, marketing, e-commerce, information retrieval, knowledge modeling, medical and biological sciences, engineering, and others. In the last few years, Graph Neural Networks (GNNs) have emerged as a promising new supervised learning framework capable of bringing the power of deep representation learning to graph and relational data. This ever-growing body of research has shown that GNNs achieve state-of-the-art performance for problems such as link prediction, fraud detection, target-ligand binding activity prediction, knowledge-graph completion, and product recommendations. The objective of this tutorial is twofold. First, it will provide an overview of the theory behind GNNs, discuss the types of problems that GNNs are well suited for, and introduce some of the most widely used GNN model architectures and problems/applications that are designed to solve. Second, it will introduce the Deep Graph Library (DGL), a new software framework that simplifies the development of efficient GNN-based training and inference programs. To make things concrete, the tutorial will provide hands-on sessions using DGL. This hands-on part will cover both basic graph applications (e.g., node classification and link prediction), as well as more advanced topics including training GNNs on large graphs and in a distributed setting. In addition, it will provide hands-on tutorials on using GNNs and DGL for real-world applications such as recommendation and fraud detection.
Anchored Model Transfer and Soft Instance Transfer for Cross-Task Cross-Domain Learning: A Study Through Aspect-Level Sentiment Classification
Authors: Yaowei Zheng, Richong Zhang, Suyuchen Wang, Samuel Mensah, Yongyi Mao
Venue: WWW
Year: 2020

Supervised learning relies heavily on readily available labelled data to infer an effective classification function. However, proposed methods under the supervised learning paradigm are faced with the scarcity of labelled data within domains, and are not generalized enough to adapt to other tasks. Transfer learning has proved to be a worthy choice to address these issues, by allowing knowledge to be shared across domains and tasks. In this paper, we propose two transfer learning methods Anchored Model Transfer (AMT) and Soft Instance Transfer (SIT), which are both based on multi-task learning, and account for model transfer and instance transfer, and can be combined into a common framework. We demonstrate the effectiveness of AMT and SIT for aspect-level sentiment classification showing the competitive performance against baseline models on benchmark datasets. Interestingly, we show that the integration of both methods AMT+SIT achieves state-of-the-art performance on the same task.
Financial Defaulter Detection on Online Credit Payment via Multi-view Attributed Heterogeneous Information Network
Authors: Qiwei Zhong, Yang Liu, Xiang Ao, Binbin Hu, Jinghua Feng, Jiayu Tang, Qing He
Venue: WWW
Year: 2020

Default user detection plays one of the backbones in credit risk forecasting and management. It aims at, given a set of corresponding features, e.g., patterns extracted from trading behaviors, predicting the polarity indicating whether a user will fail to make required payments in the future. Recent efforts attempted to incorporate attributed heterogeneous information network (AHIN) for extracting complex interactive features of users and achieved remarkable success on discovering specific default users such as fraud, cash-out users, etc. In this paper, we consider default users, a more general concept in credit risk, and propose a multi-view attributed heterogeneous information network based approach coined MAHINDER to remedy the special challenges. First, multiple views of user behaviors are adopted to learn personal profile due to the endogenous aspect of financial default. Second, local behavioral patterns are specifically modeled since financial default is adversarial and accumulated. With the real datasets contained 1.38 million users on Alibaba platform, we investigate the effectiveness of MAHINDER, and the experimental results exhibit the proposed approach is able to improve AUC over 2.8% and Recall@Precision=0.1 over 13.1% compared with the state-of-the-art methods. Meanwhile, MAHINDER has as good interpretability as tree-based methods like GBDT, which buoys the deployment in online platforms.
Recommending Themes for Ad Creative Design via Visual-Linguistic Representations
Authors: Yichao Zhou, Shaunak Mishra, Manisha Verma, Narayan Bhamidipati, Wei Wang
Venue: WWW
Year: 2020

There is a perennial need in the online advertising industry to refresh ad creatives, i.e., images and text used for enticing online users towards a brand. Such refreshes are required to reduce the likelihood of ad fatigue among online users, and to incorporate insights from other successful campaigns in related product categories. Given a brand, to come up with themes for a new ad is a painstaking and time consuming process for creative strategists. Strategists typically draw inspiration from the images and text used for past ad campaigns, as well as world knowledge on the brands. To automatically infer ad themes via such multimodal sources of information in past ad campaigns, we propose a theme (keyphrase) recommender system for ad creative strategists. The theme recommender is based on aggregating results from a visual question answering (VQA) task, which ingests the following: (i) ad images, (ii) text associated with the ads as well as Wikipedia pages on the brands in the ads, and (iii) questions around the ad. We leverage transformer based cross-modality encoders to train visual-linguistic representations for our VQA task. We study two formulations for the VQA task along the lines of classification and ranking; via experiments on a public dataset, we show that cross-modal representations lead to significantly better classification accuracy and ranking precision-recall metrics. Cross-modal representations show better performance compared to separate image and text representations. In addition, the use of multimodal information shows a significant lift over using only textual or visual information.
E-sports Report path and mode: A Case Study on China Youth Online
Authors: Wenjie Zhou, Zhe Yang Zhou
Venue: WWW
Year: 2020

In recent years, the development of China's e-sports has received more and more attention. Many influential events have caused a sensation on social media and became media events for the national carnival. As an important channel for the public to obtain news information, online media plays an indispensable role in reporting e-sports news. This article has researched China Youth Online, collected the news report focusing on e-sports from 2002 to 2019. Based on the social network analysis method and content analysis method, this study analyzed the report topics and report attitudes about e-sports. It was found that in the aspect of report topics, China Youth Online concerned about the impact of e-sports on the development of youth. This paper pointed out the deficiencies in government supervision, acknowledged the positive effects of e-sports culture, and supported the development of e-sports technology. Also, in terms of reporting attitudes, the majority of them were negative towards youth, social evaluation, e-sports industry chain and other issues, mostly e-sports issues about technology and education had positive attitudes, and mostly e-sports games were found to be neutral.
Domain Adaptive Multi-Modality Neural Attention Network for Financial Forecasting
Authors: Dawei Zhou, Lecheng Zheng, Yada Zhu, Jianbo Li, Jingrui He
Venue: WWW
Year: 2020

Financial time series analysis plays a central role in optimizing investment decision and hedging market risks. This is a challenging task as the problems are always accompanied by dual-level (i.e, data-level and task-level) heterogeneity. For instance, in stock price forecasting, a successful portfolio with bounded risks usually consists of a large number of stocks from diverse domains (e.g, utility, information technology, healthcare, etc.), and forecasting stocks in each domain can be treated as one task; within a portfolio, each stock is characterized by temporal data collected from multiple modalities (e.g, finance, weather, and news), which corresponds to the data-level heterogeneity. Furthermore, the finance industry follows highly regulated processes, which require prediction models to be interpretable, and the output results to meet compliance. Therefore, a natural research question is how to build a model that can achieve satisfactory performance on such multi-modality multi-task learning problems, while being able to provide comprehensive explanations for the end users. To answer this question, in this paper, we propose a generic time series forecasting framework named Dandelion, which leverages the consistency of multiple modalities and explores the relatedness of multiple tasks using a deep neural network. In addition, to ensure the interpretability of the framework, we integrate a novel trinity attention mechanism, which allows the end users to investigate the variable importance over three dimensions (i.e, tasks, modality and time). Extensive empirical results demonstrate that Dandelion achieves superior performance for financial market prediction across 396 stocks from 4 different domains over the past 15 years. In particular, two interesting case studies show the efficacy of Dandelion in terms of its profitability performance, and the interpretability of output results to end users.
Multilingual COVID-QA: Learning towards Global Information Sharing via Web Question Answering in Multiple Languages
Authors: Rui Yan, Weiheng Liao, Jianwei Cui, Hailei Zhang, Yichuan Hu, Dongyan Zhao
Venue: WWW
Year: 2021

Since late December 2019, it has been reported an outbreak of atypical pneumonia, now known as COVID-19 caused by the novel coronavirus. Cases have spread to more than 200 countries and regions internationally. World Health Organization (WHO) officially declares the coronavirus outbreak a pandemic and the public health emergency has caused world-wide impact to daily lives: people are advised to keep social distance, in-person events have been moved online, and some function facilitates have been locked-down. Alternatively, the Web becomes an active venue for people to share information. With respect to the on-going topic, people continuously post questions online and seek for answers. Yet, sharing global information conveyed in different languages is challenging because the language barrier is intrinsically unfriendly to monolingual speakers. In this paper, we propose a multilingual COVID-QA model to answer people’s questions in their own languages while the model is able to absorb knowledge from other languages. Another challenge is that in most cases, the information to share does not have parallel data in multiple languages. To this end, we propose a novel framework which incorporates (unsupervised) translation alignment to learn as pseudo-parallel data. Then we train multilingual question-answering mapping and generation. We demonstrate the effectiveness of our proposed approach compared against a series of competitive baselines. In this way, we make it easier to share global information across the language barriers, and hopefully we contribute to the battle against COVID-19.
Progressive, Holistic Geospatial Interlinking
Authors: George Papadakis, Georgios M. Mandilaras, Nikos Mamoulis, Manolis Koubarakis
Venue: WWW
Year: 2021

Geospatial data constitute a considerable part of Semantic Web data, but at the moment, its sources are inadequately interlinked with topological relations in the Linked Open Data cloud. Geospatial Interlinking covers this gap with batch techniques that are restricted to individual topological relations, even though most operations are common for all main relations. In this work, we introduce a batch algorithm that simultaneously computes all topological relations and define the task of Progressive Geospatial Interlinking, which produces results in a pay-as-you-go manner when the available computational or temporal resources are limited. We propose two progressive algorithms and conduct a thorough experimental study over large, real datasets, demonstrating the superiority of our techniques over the current state-of-the-art.
Robust Network Alignment via Attack Signal Scaling and Adversarial Perturbation Elimination
Authors: Yang Zhou, Zeru Zhang, Sixing Wu, Victor S. Sheng, Xiaoying Han, Zijie Zhang, Ruoming Jin
Venue: WWW
Year: 2021

Recent studies have shown that graph learning models are highly vulnerable to adversarial attacks, and network alignment methods are no exception. How to enhance the robustness of network alignment against adversarial attacks remains an open research problem. In this paper, we propose a robust network alignment solution, RNA, for offering preemptive protection of existing network alignment algorithms, enhanced with the guidance of effective adversarial attacks. First, we analyze how popular iterative gradient-based adversarial attack techniques suffer from gradient vanishing issues and show a fake sense of attack effectiveness. Based on dynamical isometry theory, an attack signal scaling (ASS) method with established upper bound of feasible signal scaling is introduced to alleviate the gradient vanishing issues for effective adversarial attacks while maintaining the decision boundary of network alignment. Second, we develop an adversarial perturbation elimination (APE) model to neutralize adversarial nodes in vulnerable space to adversarial-free nodes in safe area, by integrating Dirac delta approximation (DDA) techniques and the LSTM models. Our proposed APE method is able to provide proactive protection to existing network alignment algorithms against adversarial attacks. The theoretical analysis demonstrates the existence of an optimal distribution for the APE model to reach a lower bound. Last but not least, extensive evaluation on real datasets presents that RNA is able to offer the preemptive protection to trained network alignment methods against three popular adversarial attack models.
Constructing a Comparison-based Click Model for Web Search
Authors: Ruizhe Zhang, Xiaohui Xie, Jiaxin Mao, Yiqun Liu, Min Zhang, Shaoping Ma
Venue: WWW
Year: 2021

Extracting valuable feedback information from user behavior logs is one of the major concerns in Web search studies. Among the tremendous efforts that aim to improve search performance with user behavior modeling, constructing click models is of vital importance because it provides a direct estimation of result relevance. Most existing click models assume that whether or not users click on results only depends on the examination probability and the content of the result. However, through a carefully designed user eye-tracking study, we found that users do not make click-through decisions in isolation. Instead, they also consider the context of a result (e.g., adjacent results). This finding leads to the design of a novel click model named Comparison-based Click Model (CBCM). Different from traditional examination hypotheses, CBCM introduces the concept of an examination viewport and assumes users click results after comparing adjacent results within the same viewport. The experimental results on a publicly available user behavior dataset demonstrate the effectiveness of CBCM. We also public our code of CBCM and dataset.