OpenRaiser/Intern-Atlas huggingface dataset:
Training Machine Learning Models With Causal Logic
Authors: Ang Li, Suming J. Chen, Jingzheng Qin, Zhen Qin
Venue: WWW
Year: 2020

Machine-learning (ML) models are ubiquitously used to make a variety of inferences, a common application being to predict and categorize user behavior. However, ML models often suffer from only being exposed to biased data – for instance, a search ranking model that uses clicks to determine how to rank will suffer from position bias. The difficulty arises due to user feedback only being observed for one treatment and not existing counterfactually for other potential treatments. In this work, we discuss a real-world situation in which a binary classification model is used in production in order to make decisions about how to treat users. We introduce the model and discuss the limitations of our modeling approach. We show that by using unit selection criterion we can do a better job classifying users. Following, we propose a causal modeling method in which we can take the existing data and use it to derive bounds that can be used to modify the objective function in order to incorporate causal learning into our training process. We demonstrate the effectiveness of this approach in a real-world setting.
Collaboration of Open Content News in Wikipedia: The Role and Impact of Gatekeepers
Authors: Ang Li, Rosta Farzan
Venue: WWW
Year: 2020

With the advances of social technologies, open content in social media has become an important place where people gather and communicate news. Prior studies in conventional mass media has long studied the news reporting process, and suggest that gatekeepers – editors or journalists who control the information and have a unique power to determine what gets published to the public – play an important role in the news reporting process. However, as the process of how open content are created by contributors in social media platforms is different, what we understand about content publication process in traditional mass media can not directly apply in the context of social media. Especially, it is unclear who are the gatekeepers and how do they influence the content creation and spread of information in social media. In the current proposed study, I aim to understand this new model of content generation process through the lens of gatekeepers in social media platforms such as Wikipedia. Specifically, I aim to discover ways to identify gatekeepers and assess their impact on information quality and content polarization.
Directional and Explainable Serendipity Recommendation
Authors: Xueqi Li, Wenjun Jiang, Weiguang Chen, Jie Wu, Guojun Wang, Kenli Li
Venue: WWW
Year: 2020

Serendipity recommendation has attracted more and more attention in recent years; it is committed to providing recommendations which could not only cater to users’ demands but also broaden their horizons. However, existing approaches usually measure user-item relevance with a scalar instead of a vector, ignoring user preference direction, which increases the risk of unrelated recommendations. In addition, reasonable explanations increase users’ trust and acceptance, but there is no work to provide explanations for serendipitous recommendations. To address these limitations, we propose a Directional and Explainable Serendipity Recommendation method named DESR. Specifically, we extract users’ long-term preferences with an unsupervised method based on GMM (Gaussian Mixture Model) and capture their short-term demands with the capsule network at first. Then, we propose the serendipity vector to combine long-term preferences with short-term demands and generate directionally serendipitous recommendations with it. Finally, a back-routing scheme is exploited to offer explanations. Extensive experiments on real-world datasets show that DESR could effectively improve the serendipity and explainability, and give impetus to the diversity, compared with existing serendipity-based methods.
Fairness of Classification Using Users' Social Relationships in Online Peer-To-Peer Lending
Authors: Yanying Li, Yue Ning, Rong Liu, Ying Wu, Wendy Hui Wang
Venue: WWW
Year: 2020

Peer-to-peer (P2P) lending marketplaces on the Web have been growing over the last decade. By providing online platforms, P2P lending enables individuals to borrow and lend money directly from and to one another. Since the applicants on P2P lending platforms may lack sufficient financial history for assessment, quite a few P2P lending service providers have been utilizing the applicants’ social relationships to improve the risk prediction accuracy of loan applications. However, utilizing the information of applicants’ social relationships may introduce discrimination in prediction. In this paper, we analyze and evaluate the impact of the applicants’ social relationships on the fairness of risk prediction for P2P lending. We investigate over a million loan records collected from Prosper.com, one of the leading P2P lending companies in the world. We construct the Prosper social network of loan borrowers and lenders, and generate the social features of applicants by adapting a state-of-the-art social credit scoring scheme to the Prosper social network. We consider two types of fairness notions in the literature, namely individual fairness and counterfactual fairness. Our results demonstrate that the social score harms both individual and counterfactual fairness of classification. To address this issue, we design two new algorithms that mitigate bias by generalizing social features. Our experimental results show that our mitigation algorithms can reduce bias while utilizing social scores effectively.
Stabilizing Neural Search Ranking Models
Authors: Ruilin Li, Zhen Qin, Xuanhui Wang, Suming J. Chen, Donald Metzler
Venue: WWW
Year: 2020

Neural search ranking models have been not only actively studied in the information retrieval community, but also widely adopted in real-world industrial applications. However, due to the non-convexity and stochastic training of neural model formulations, the obtained models are unstable in the sense that model predictions can vary a lot for two models trained with the same configuration. In practice, new features are continuously introduced and new model architectures are explored to improve model effectiveness. In these cases, the instability of neural models leads to unnecessary document ranking changes for a large portion of queries. Such changes not only lead to inconsistent user experience, but also add noise to online experimentation and can slow down model improvement cycles. How to stabilize neural search ranking models during model update is an important but largely unexplored problem. Motivated by trigger analysis, we suggest balancing the trade-off between performance improvement and the number of affected queries. Concretely, we formulate it as an optimization problem with the objective as maximizing the average effect over the affected queries. We propose two heuristics and one theory-guided stabilization method to solve the optimization problem. Our proposed methods are evaluated on two of the world’s largest personal search services: Gmail search and Google Drive search. Empirical results show that our proposed methods are very effective in optimizing the proposed objective and are applicable to different model update scenarios.
MetaNER: Named Entity Recognition with Meta-Learning
Authors: Jing Li, Shuo Shang, Ling Shao
Venue: WWW
Year: 2020

Recent neural architectures in named entity recognition (NER) have yielded state-of-the-art performance on single domain data such as newswires. However, they still suffer from (i) requiring massive amounts of training data to avoid overfitting; (ii) huge performance degradation when there is a domain shift in the data distribution between training and testing. In this paper, we investigate the problem of domain adaptation for NER under homogeneous and heterogeneous settings. We propose MetaNER, a novel meta-learning approach for domain adaptation in NER. Specifically, MetaNER incorporates meta-learning and adversarial training strategies to encourage robust, general and transferable representations for sequence labeling. The key advantage of MetaNER is that it is capable of adapting to new unseen domains with a small amount of annotated data from those domains. We extensively evaluate MetaNER on multiple datasets under homogeneous and heterogeneous settings. The experimental results show that MetaNER achieves state-of-the-art performance against eight baselines. Impressively, MetaNER surpasses the in-domain performance using only 16.17% and 34.76% of target domain data on average for homogeneous and heterogeneous settings, respectively.
Do podcasts and music compete with one another? Understanding users' audio streaming habits
Authors: Ang Li, Alice Wang, Zahra Nazari, Praveen Chandar, Benjamin A. Carterette
Venue: WWW
Year: 2020

Over the past decade, podcasts have been one of the fastest growing online streaming media. Many online audio streaming platforms such as Pandora, Spotify, etc. that traditionally focused on music content have started to incorporate services related to podcasts. Although incorporating new media types such as podcasts has created tremendous opportunities for these streaming platforms to expand their content offering, it also introduces new challenges. Since the functional use of podcasts and music may largely overlap for many people, the two types of content may compete with one another for the finite amount of time that users may allocate for audio streaming. As a result, incorporating podcast listening may influence and change the way users have originally consumed music. Adopting quasi-experimental techniques, the current study assesses the causal influence of adding a new class of content on user listening behavior by using large scale observational data collected from a widely used audio streaming platform. Our results demonstrate that podcast and music consumption compete slightly but do not replace one another – users open another time window to listen to podcasts. In addition, users who have added podcasts to their music listening demonstrate significantly different consumption habits for podcasts vs. music in terms of the streaming time, duration and frequency. Taking all the differences as input features to a machine learning model, we demonstrate that a podcast listening session is predictable at the start of a new listening session. Our study provides a novel contribution for online audio streaming and consumption services to understand their potential consumers and to best support their current users with an improved recommendation system.
Few-Shot Learning for New User Recommendation in Location-based Social Networks
Authors: Ruirui Li, Xian Wu, Xiusi Chen, Wei Wang
Venue: WWW
Year: 2020

The proliferation of GPS-enabled devices establishes the prosperity of location-based social networks, which results in a tremendous amount of user check-ins. These check-ins bring in preeminent opportunities to understand users’ preferences and facilitate matching between users and businesses. However, the user check-ins are extremely sparse due to the huge user and business bases, which makes matching a daunting task. In this work, we investigate the recommendation problem in the context of identifying potential new customers for businesses in LBSNs. In particular, we focus on investigating the geographical influence, composed of geographical convenience and geographical dependency. In addition, we leverage metric-learning-based few-shot learning to fully utilize the user check-ins and facilitate the matching between users and businesses. To evaluate our proposed method, we conduct a series of experiments to extensively compare with 13 baselines using two real-world datasets. The results demonstrate that the proposed method outperforms all these baselines by a significant margin.
Fighting Against Deepfake: Patch&Pair Convolutional Neural Networks (PPCNN)
Authors: Xurong Li, Kun Yu, Shouling Ji, Yan Wang, Chunming Wu, Hui Xue
Venue: WWW
Year: 2020

In this paper, we propose a novel Patch&Pair Convolutional Neural Networks (PPCNN) to distinguish Deepfake videos or images from real ones. Through the comprehensive evaluations on public datasets, we demonstrate that our model performs better than existing detection methods and show better generalization.
Adaptive Probabilistic Word Embedding
Authors: Shuangyin Li, Yu Zhang, Rong Pan, Kaixiang Mo
Venue: WWW
Year: 2020

Word embeddings have been widely used and proven to be effective in many natural language processing and text modeling tasks. It is obvious that one ambiguous word could have very different semantics in various contexts, which is called polysemy. Most existing works aim at generating only one single embedding for each word while a few works build a limited number of embeddings to present different meanings for each word. However, it is hard to determine the exact number of senses for each word as the word meaning is dependent on contexts. To address this problem, we propose a novel Adaptive Probabilistic Word Embedding (APWE) model, where the word polysemy is defined over a latent interpretable semantic space. Specifically, at first each word is represented by an embedding in the latent semantic space and then based on the proposed APWE model, the word embedding can be adaptively adjusted and updated based on different contexts to obtain the tailored word embedding. Empirical comparisons with state-of-the-art models demonstrate the superiority of the proposed APWE model.
Personalized Ranking with Importance Sampling
Authors: Defu Lian, Qi Liu, Enhong Chen
Venue: WWW
Year: 2020

As the task of predicting a personalized ranking on a set of items, item recommendation has become an important way to address information overload. Optimizing ranking loss aligns better with the ultimate goal of item recommendation, so many ranking-based methods were proposed for item recommendation, such as collaborative filtering with Bayesian Personalized Ranking (BPR) loss, and Weighted Approximate-Rank Pairwise (WARP) loss. However, the ranking-based methods can not consistently beat regression-based models with the gravity regularizer. The key challenge in ranking-based optimization is difficult to fully use the limited number of negative samples, particularly when they are not so informative. To this end, we propose a new ranking loss based on importance sampling so that more informative negative samples can be better used. We then design a series of negative samplers from simple to complex, whose informativeness of negative samples is from less to more. With these samplers, the loss function is easy to use and can be optimized by popular solvers. The proposed algorithms are evaluated with five real-world datasets of varying size and difficulty. The results show that they consistently outperform the state-of-the-art item recommendation algorithms, and the relative improvements with respect to NDCG@50 are more than 19.2% on average. Moreover, the loss function is verified to make better use of negative samples and to require fewer negative samples when they are more informative.
Network Analysis on Emotional Responses in International Disputes
Authors: Dongwoo Lim, Fujio Toriumi, Kaori Hayashi
Venue: WWW
Year: 2020

In this study, we describe different aspects of nationalism revealed on Twitter in Japan and Korea during a recent international dispute on the trade conflict. We conducted a network analysis of 23 million tweets collected in July 2019, during a bitter economic dispute between Japan and South Korea. As a result, we could identify different emotional responses between two countries. Japan’s retweeted relationship was divided into two large groups – conservatives and liberals – in terms of political orientation, and Korea’s follow-up relationship was consisted of one large group that shared a sense of nationalism regardless of political orientation. Furthermore, we created a co-occurrence network to identify which words were frequently used in each distinctive group.
Customizing Your Own Route with QQIP. A Quantitative and Qualitative Itinerary Planner for New Transportation Routes
Authors: Fandel Lin, Jie-Yu Fang, Hsun-Ping Hsieh
Venue: WWW
Year: 2020

Public transportation route planning is crucial for both traffic management authority and residents. Current procedures for deciding new routes are time-consuming and ineffective due to the complicate simulation process or overwhelming numbers of opinions from stockholders. In this paper, we propose a novel decision supporting tool, Quantitative and Qualitative Itinerary Planner (QQIP), to help governments pre-evaluate new route services in the city in a timely manner. The function of QQIP is three-fold: visualization of urban informatics, a flexible interface for sketching designate routes, and passenger flows estimation in certain time intervals. With acquired relevant urban information, user can pre-estimate the effectiveness of designed routes using QQIP. To capture the spatial-temporal factors correlated with passenger flows, we propose route-affecting region (RAR) and adopt Deep Neural Network (DNN) framework to combine several dynamic and static features. According to our experimental results on bus-ticket data of Tainan city, the proposed RAR-based feature engineering methods are effective for handling and combining high-correlated dynamic and static data; meanwhile, QQIP can help decision makers infer the passenger flow effectively and efficiently for given designated routes.
A Convolutional Approach for Estimating Popularity of New Branch Stores
Authors: Fandel Lin, Hsun-Ping Hsieh
Venue: WWW
Year: 2020

Thousands of shops are opened and closed down constantly every day. Determining the location of a new branch store is a critical issue for retail business. Making decisions for locations of new stores has been widely studied in many fields considering cost saving and profits. In this study, we propose the “Grid-liked Graph” (GLG), a graph-based approach for spatial data division to model the spatial distribution of urban features. Based on the proposed GLG, four footprint features are extracted from location-based social network data and treated as image features. Convolution Neural Network (CNN)-based models including LeNet and AlexNet are then adopted to infer the popularity of each candidate store for optimizing location selection. According to our experimental results on two kinds of retail stores, the proposed methods can increase usage of data, and can also perform well for datasets with fewer features compared with previous studies.
Traffic Light Control with Real-Time Vehicle License Plate Recognition
Authors: Fandel Lin, Shiuan-Tyng Lin, Jie-Yu Fang, Hsun-Ping Hsieh
Venue: WWW
Year: 2020

Traffic light control is one of crucial issues in the traffic deployment. However, the current procedure in generating traffic light schedule that relies on either empirical law manually or simulating software is time-consuming and lack of both flexibility and immediacy. Given real-time traffic flow information from vehicle license plate recognition sensors, the proposed framework can immediately generate a traffic light schedule to relieve arising or probable traffic congestion which was not expected at first by management authorities. The novelty of our system is that it combines the instant traffic flow transition estimation and an adaptation of Gaussian Mixture Model. Preliminary evaluations show that proposed method outperforms the current procedure that the traffic management authority in Tainan City takes.
OutfitNet: Fashion Outfit Recommendation with Attention-Based Multiple Instance Learning
Authors: Yusan Lin, Maryam Moosaei, Hao Yang
Venue: WWW
Year: 2020

Recommending fashion outfits to users presents several challenges. First of all, an outfit consists of multiple fashion items, and each user emphasizes different parts of an outfit when considering whether they like it or not. Secondly, a user’s liking for a fashion outfit considers not only the aesthetics of each item but also the compatibility among them. Lastly, fashion outfit data is often sparse in terms of the relationship between users and fashion outfits. Not to mention, we can only obtain what the users like, but not what they dislike. To address the above challenges, in this paper, we formulate the fashion outfit recommendation problem as a multiple-instance-learning (MIL) problem. We propose OutfitNet, a fashion outfit recommendation framework that includes two stages. The first stage is a Fashion Item Relevancy network (FIR), which learns the compatibility between fashion items and further generates relevancy embedding of fashion items. In the second stage, an Outfit Preference network (OP) learns the users’ tastes for fashion outfits using visual information. OutfitNet takes in multiple fashion items in a fashion outfit as input, learns the compatibility among fashion items, the users’ tastes toward each item, as well as the users’ attention on different items in the outfit with the attention mechanism. Quantitatively, our experiments show that OutfitNet outperforms state-of-the-art models in two tasks: fill-in-the-blank (FITB) and personalized outfit recommendation. Qualitatively, we demonstrate that the learned personalized item scores and attention scores capture well the users’ fashion tastes, and the learned fashion item embeddings capture well the compatibility relationships among fashion items. We also leverage the learned fashion item embedding and propose a simple fashion outfit generation framework, which is shown to produce high-quality fashion outfit combinations.
Keeping Passwords In Your Pocket: Managing Password Locally With Mobile Fingerprint Sensors
Authors: Pin-Yen Lin, Zhong-Yi Zhou, Chun-Ming Chang, Hung-Wei Chen, Shu-Po Tung, Hsu-Chun Hsiao
Venue: WWW
Year: 2020

Password managers allow users to use strong and unique passwords online. However, users have to either trust a third-party service to manage their passwords or manually synchronize passwords among local devices. To relax the trust assumption while maintaining usability, in this work, we develop a password manager that stores passwords on mobile phones. The passwords can be easily unlocked via the user’s fingerprint and securely sent to other devices such as a desktop browser. Our implementation uses the iOS local authentication API for biological authentication, and uses firebase cloud messaging to send the password to the browser.
Dual-attentional Factorization-Machines based Neural Network for User Response Prediction
Authors: Feng Liu, Wei Guo, Huifeng Guo, Ruiming Tang, Yunming Ye, Xiuqiang He
Venue: WWW
Year: 2020

This paper proposes Dual-attentional Factorization-Machines (DFM), which incorporates global-wise attention and element-wise attention with FM for user response prediction. We further extend DFM with a deep neural network and name this new model Dual-attentional Factorization-machines based Network (DFNet). Comprehensive experiments are conducted on two real-world datasets to demonstrate the effectiveness of DFM and DFNet over the state-of-the-art models for user response prediction.