OpenRaiser/Intern-Atlas huggingface dataset:
Cross-Team Collaboration and Diversity in the Bridge2AI Project
Authors: Huimin Xu, Chitrank Gupta, Zhandos Sembay, Swathi Thaker, Pamela Payne-Foster, Jake Y. Chen, Ying Ding
Venue: WWW
Year: 2023

The Bridge2AI project, funded by the National Institutes of Health, involves researchers from different disciplines and backgrounds to develop well-curated AI health data and tools. Understanding cross-disciplinary and cross-organizational collaboration at the individual, team, and project levels is critical. In this paper, we matched Bridge2AI team members to the PubMed Knowledge dataset to get their health-related publications. We built the collaboration network for Bridge2AI members and all of their collaborators and sorted out researchers with the largest degree of centrality and betweenness centrality. Our finding suggests that Bridge2AI members need to strengthen internal collaborations and boost mutual understanding in this project. We also applied machine learning methods to cluster all the researchers and labeled publication topics in different clusters. Finally, by identifying the gender/racial diversity of researchers, we found that teams with higher racial diversity receive more citations, and individuals with diverse gender collaborators publish more papers.
Simple Multi-view Can Bring Powerful Graph Neural Network
Authors: Bingbing Xu, Yang Li, Qi Cao, Huawei Shen
Venue: WWW
Year: 2023

Graph neural networks have achieved state-of-the-art performance on graph-related tasks through layer-wise neighborhood aggregation. Previous works aim to achieve powerful capability via designing injective neighborhood aggregation functions in each layer, which is difficult to determine and numerous additional parameters make it difficult to train these models. It is the input space and the aggregation function that achieve powerful capability at the same time. Instead of designing complexity aggregation functions, we propose a simple and effective framework, namely MV-GNN, to improve the model expressive power via constructing the new input space. Precisely, MV-GNN samples multi-view subgraphs for each node, and any GNN model can be applied to these views. The representation of target node is finally obtained via aggregating all views injectively. Two typical GNNs (i.e., GCN and GAT) are adopted as base models in the proposed framework, and we demonstrate the effectiveness of MV-GNN through extensive experiments.
Foreword for Workshop on Decision Making for Information Retrieval and Recommender Systems
Authors: Da Xu, Tobias Schnabel, Xiquan Cui, Sarah Dean, Aniket Deshmukh, Bo Yang, Shipeng Yu
Venue: WWW
Year: 2023

1 FOREWORD Most of the recent progress in information retrieval (IR) and recommender systems has been fueled by deep learning: the success of neural networks has brought tremendous opportunities to model highly complex patterns for prediction. However, algorithmic advances on accurate predictions and improved user modeling are just a small part of designing considerations of a much larger system. IR and recommender systems difer from other machine learning domains because they are inherently part of an ecosystem – in the simplest case, a world of items and users. In these ecosystems, system designers face a broad range of decisions – e.g., how to balance popularity, which incentives should be given to which users, or what safeguards to put in place to ensure the platform thrives in the long-run. Some of the decision making problems are statistical and algorithmic in nature, such as coping with the uncertainty of data and models, while others can involve building systems for the satisfactory of multiple parties. In general, there are many complex decision-making challenges faced by real-world IR and recommender systems, but existing approaches often make oversimplifed assumptions about the environment, data, and human behavior. Ignoring those challenges or treating them simply as pattern recognition problems can cost the engagement, accessibility, fairness, inclusiveness, and ultimately the vitality of IR and recommendation systems.
Towards Deeper Graph Neural Networks via Layer-Adaptive
Authors: Bingbing Xu, Bin Xie, Huawei Shen
Venue: WWW
Year: 2023

Graph neural networks have achieved state-of-the-art performance on graph-related tasks. Previous methods observed that GNNs’ performance degrades as the number of layers increases and attributed this phenomenon to over-smoothing caused by the stacked propagation. However, we proved experimentally and theoretically that it is overfitting rather than propagation that causes performance degradation. We propose a novel framework: layer-adaptive GNN (LAGNN) consisting of two modules: adaptive layer selection and random Droplayer, which can adaptively determine the number of layers and thus alleviate overfitting. We attached this general framework to two representative GNNs and achieved consistency improvements on six representative datasets.
Robust Stochastic Multi-Armed Bandits with Historical Data
Authors: Sarah Boufelja Yacobi, Djallel Bouneffouf
Venue: WWW
Year: 2023

We consider the problem of Stochastic Contextual Multi-Armed Bandits (CMABs) initialised with Historical data. Initialisation with historical data is an example of data-driven regularisation which should, in theory, accelerate the convergence of CMABs. However, in practice, we have little to no control over the underlying generation process of such data, which may exhibit some pathologies, possibly impeding the convergence and the stability of the algorithm. In this paper, we focus on two main challenges: bias selection and data corruption. We propose two new algorithms to solve these specific issues: LinUCB with historical data and offline balancing (OB-HLinUCB) and Robust LinUCB with corrupted historical data (R-HLinUCB). We derive their theoretical regret bounds and discuss their computational performance using real-world datasets.
A Detection System for Comfortable Locations Based on Facial Expression Analysis While Riding Bicycles
Authors: Ryuta Yamaguchi, Panote Siriaraya, Tomoki Yoshihisa, Shinji Shimojo, Yukiko Kawai
Venue: WWW
Year: 2023

In recent years, the use of bicycle as a healthy and economical means of transportation has been promoted worldwide. In addition, with the increase in bicycle commuting due to the COVID-19, the use of bicycles are attracting attention as a last-mile means of transportation in Mobility as a Service(MaaS). To help ensure a safe and comfortable ride using a smartphone mounted on a bicycle, this study focuses on analyzing facial expressions while riding to determine potential comfort along the route with the surrounding environment and to provide a map that users can explicitly feedback(FB) after riding. Combining the emotions of facial expressions while riding and FB, we annotate comfort to different locations. Afterwards, we verify the relationship between locations with high level of comfort based on the acquired data and the surrounding environment of those locations using Google Street View(GSV).
Bad Apples: Understanding the Centralized Security Risks in Decentralized Ecosystems
Authors: Kailun Yan, Jilian Zhang, Xiangyu Liu, Wenrui Diao, Shanqing Guo
Venue: WWW
Year: 2023

The blockchain-powered decentralized applications and systems have been widely deployed in recent years. The decentralization feature promises users anonymity, security, and non-censorship, which is especially welcomed in the areas of decentralized finance and digital assets. From the perspective of most common users, a decentralized ecosystem means every service follows the principle of decentralization. However, we find that the services in a decentralized ecosystem still may contain centralized components or scenarios, like third-party SDKs and privileged operations, which violate the promise of decentralization and may cause a series of centralized security risks. In this work, we systematically study the centralized security risks existing in decentralized ecosystems. Specifically, we identify seven centralized security risks in the deployment of two typical decentralized services – crypto wallets and DApps, such as anonymity loss and overpowered owner. Also, to measure these risks in the wild, we designed an automated detection tool called Naga and carried out large-scale experiments. Based on the measurement of 28 Ethereum crypto wallets (Android version) and 110,506 on-chain smart contracts, the result shows that the centralized security risks are widespread. Up to 96.4% of wallets and 83.5% of contracts exist at least one security risk, including 260 well-known tokens with a total market cap of over $98 billion.
DeepPvisit: A Deep Survival Model for Notification Management
Authors: Guangyu Yang, Efrem Ghebreab, Jiaxi Xu, Xianen Qiu, Yiping Yuan, Wensheng Sun
Venue: WWW
Year: 2023

Notification is a core feature of mobile applications. They inform users about a variety of events happening in the communities. Users may take immediate action to visit the app or ignore the notifications depending on the timing and the relevance of a notification to the user. In this paper, we present the design, implementation, and evaluation of DeepPvisit, a novel probabilistic deep learning survival method for modeling interactions between a user visit and a mobile notification decision, targeting notification volume and delivery time optimization, and driving long-term user engagements. Offline evaluations and online A/B test experiments show DeepPvisit outperforms the existing survival regression model and the other baseline models and delivers better business metrics online.
Visual-Aware Testing and Debugging for Web Performance Optimization
Authors: Xinlei Yang, Wei Liu, Hao Lin, Zhenhua Li, Feng Qian, Xianlong Wang, Yunhao Liu, Tianyin Xu
Venue: WWW
Year: 2023

Web performance optimization services, or web performance optimizers (WPOs), play a critical role in today’s web ecosystem by improving page load speed and saving network traffic. However, WPOs are known for introducing visual distortions that disrupt the users’ web experience. Unfortunately, visual distortions are hard to analyze, test, and debug, due to their subjective measure, dynamic content, and sophisticated WPO implementations. This paper presents Vetter, a novel and effective system that automatically tests and debugs visual distortions. Its key idea is to reason about the morphology of web pages, which describes the topological forms and scale-free geometrical structures of visual elements. Vetter efficiently calculates morphology and comparatively analyzes the morphologies of web pages before and after a WPO, which acts as a differential test oracle. Such morphology analysis enables Vetter to detect visual distortions accurately and reliably. Vetter further diagnoses the detected visual distortions to pinpoint the root causes in WPOs’ source code. This is achieved by morphological causal inference, which localizes the offending visual elements that trigger the distortion and maps them to the corresponding code. We applied Vetter to four representative WPOs. Vetter discovers 21 unknown defects responsible for 98% visual distortions; 12 of them have been confirmed and 5 have been fixed.
Movie Ticket, Popcorn, and Another Movie Next Weekend: Time-Aware Service Sequential Recommendation for User Retention
Authors: Xiaoyan Yang, Dong Wang, Binbin Hu, Dan Yang, Yue Shen, Jinjie Gu, Zhiqiang Zhang, Shiwei Lyu, Haipeng Zhang, Guannan Zhang
Venue: WWW
Year: 2023

When a customer sees a movie recommendation, she may buy the ticket right away, which is the immediate feedback that helps improve the recommender system. Alternatively, she may choose to come back later and this long-term feedback is also modeled to promote user retention. However, the long-term feedback comes with non-trivial challenges in understanding user retention: the complicated correlation between current demands and follow-up demands, coupled with the periodicity of services. For instance, before the movie, the customer buys popcorn through the App, which temporally correlates with the initial movie recommendation. Days later, she checks the App for new movies, as a weekly routine. To address this complexity in a more fine-grained revisit modeling, we propose Time Aware Service Sequential Recommendation (TASSR) for user retention, which is equipped with a multi-task design and an In-category TimeSeqBlock module. Large-scale online and offline experiments demonstrate its significant advantages over competitive baselines.
Graph Neural Networks without Propagation
Authors: Liang Yang, Qiuliang Zhang, Runjie Shi, Wenmiao Zhou, Bingxin Niu, Chuan Wang, Xiaochun Cao, Dongxiao He, Zhen Wang, Yuanfang Guo
Venue: WWW
Year: 2023

Due to the simplicity, intuition and explanation, most Graph Neural Networks (GNNs) are proposed by following the pipeline of message passing. Although they achieve superior performances in many tasks, propagation-based GNNs possess three essential drawbacks. Firstly, the propagation tends to produce smooth effect, which meets the inductive bias of homophily, and causes two serious issues: over-smoothing issue and performance drop on networks with heterophily. Secondly, the propagations to each node are irrelevant, which prevents GNNs from modeling high-order relation, and cause the GNNs fragile to the attributes noises. Thirdly, propagation-based GNNs may be fragile to topology noise, since they heavily relay on propagation over the topology. Therefore, the propagation, as the key component of most GNNs, may be the essence of some serious issues in GNNs. To get to the root of these issue, this paper attempts to replace the propagation with a novel local operation. Quantitative experimental analysis reveals: 1) the existence of low-rank characteristic in the node attributes from ego-networks and 2) the performance improvement by reducing its rank. Motivated by this finding, this paper propose the Low-Rank GNNs, whose key component is the low-rank attribute matrix approximation in ego-network. The graph topology is employed to construct the ego-networks instead of message propagation, which is sensitive to topology noises. The proposed Low-Rank GNNs posses some attractive characteristics, including robust to topology and attribute noises, parameter-free and parallelizable. Experimental evaluations demonstrate the superior performance, robustness to noises and universality of the proposed Low-Rank GNNs.
Path-specific Causal Fair Prediction via Auxiliary Graph Structure Learning
Authors: Liuyi Yao, Yaliang Li, Bolin Ding, Jingren Zhou, Jinduo Liu, Mengdi Huai, Jing Gao
Venue: WWW
Year: 2023

With ubiquitous adoption of machine learning algorithms in web technologies, such as recommendation system and social network, algorithm fairness has become a trending topic, and it has a great impact on social welfare. Among different fairness definitions, path-specific causal fairness is a widely adopted one with great potentials, as it distinguishes the fair and unfair effects that the sensitive attributes exert on algorithm predictions. Existing methods based on path-specific causal fairness either require graph structure as the prior knowledge or have high complexity in the calculation of path-specific effect. To tackle these challenges, we propose a novel casual graph based fair prediction framework which integrates graph structure learning into fair prediction to ensure that unfair pathways are excluded in the causal graph. Furthermore, we generalize the proposed framework to the scenarios where sensitive attributes can be non-root nodes and affected by other variables, which is commonly observed in real-world applications, such as recommendation system, but hardly addressed by existing works. We provide theoretical analysis on the generalization bound for the proposed fair prediction method, and conduct a series of experiments on real-world datasets to demonstrate that the proposed framework can provide better prediction performance and algorithm fairness trade-off.
A Knowledge Enhanced Hierarchical Fusion Network for CTR Prediction under Account Search Scenario in WeChat
Authors: Yuanzhou Yao, Zhao Zhang, Kaijia Yang, Huasheng Liang, Qiang Yan, Fuzhen Zhuang, Yongjun Xu, Boyu Diao, Chao Li
Venue: WWW
Year: 2023

Click-through rate (CTR) estimation plays as a pivotal function module in various online services. Previous studies mainly apply CTR models to the field of recommendation or online advertisement. Indeed, CTR is also critical in information retrieval, since the CTR probability can serve as a valuable feature for a query-document pair. In this paper, we study the CTR task under account search scenario in WeChat, where users search official accounts or mini programs corresponding to an organization. Despite the large number of CTR models, directly applying them to our task is inappropriate since the account retrieval task has a number of specific characteristics. E.g., different from traditional user-centric CTR models, in our task, CTR prediction is query-centric and does not model user information. In addition, queries and accounts are short texts, and heavily rely on prior knowledge and semantic understanding. These characteristics require us to specially design a CTR model for the task. To this end, we propose a novel CTR prediction model named Knowledge eNhanced hIerarchical Fusion nEtwork (KNIFE). Specifically, to tackle the prior information problem, we mine the knowledge graph of accounts as side information; to enhance the representations of queries, we construct a bipartite graph for queries and accounts. In addition, a hierarchical network structure is proposed to fuse the representations of different information in a fine-grained manner. Finally, the representations of queries and accounts are obtained from this hierarchical network and fed into the CTR model together with other features for prediction. We conduct extensive experiments against 12 existing models across two industrial datasets. Both offline and online A/B test results indicate the effectiveness of KNIFE.
Show Me The Best Outfit for A Certain Scene: A Scene-aware Fashion Recommender System
Authors: Tangwei Ye, Liang Hu, Qi Zhang, Zhongyuan Lai, Usman Naseem, Dora D. Liu
Venue: WWW
Year: 2023

Fashion recommendation (FR) has received increasing attention in the research of new types of recommender systems. Existing fashion recommender systems (FRSs) typically focus on clothing item suggestions for users in three scenarios: 1) how to best recommend fashion items preferred by users; 2) how to best compose a complete outfit, and 3) how to best complete a clothing ensemble. However, current FRSs often overlook an important aspect when making FR, that is, the compatibility of the clothing item or outfit recommendations is highly dependent on the scene context. To this end, we propose the scene-aware fashion recommender system (SAFRS), which uncovers a hitherto unexplored avenue where scene information is taken into account when constructing the FR model. More specifically, our SAFRS addresses this problem by encoding scene and outfit information in separation attention encoders and then fusing the resulting feature embeddings via a novel scene-aware compatibility score function. Extensive qualitative and quantitative experiments are conducted to show that our SAFRS model outperforms all baselines for every evaluated metric.
Online Advertising in Ukraine and Russia During the 2022 Russian Invasion
Authors: Christina Yeung, Umar Iqbal, Yekaterina Tsipenyuk O&apos, Neil, Tadayoshi Kohno, Franziska Roesner
Venue: WWW
Year: 2023

Online ads are a major source of information on the web. The mass reach of online advertising is often leveraged for information dissemination, at times with an objective to influence public opinion (e.g., election misinformation). We hypothesized that online advertising, due to its reach and potential, might have been used to spread information around the 2022 Russian invasion of Ukraine. Thus, to understand the online ad ecosystem during this conflict, we conducted a five-month long large-scale measurement study of online advertising in Ukraine, Russia, and the US. We studied advertising trends of ad platforms that delivered ads in Ukraine, Russia, and the US and conducted an in-depth qualitative analysis of the conflict-related ad content. We found that prominent US-based advertisers continued to support Russian websites, and a portion of online ads were used to spread conflict-related information, including protesting the invasion, and spreading awareness, which might have otherwise potentially been censored in Russia.
Tracing Knowledge Instead of Patterns: Stable Knowledge Tracing with Diagnostic Transformer
Authors: Yu Yin, Le Dai, Zhenya Huang, Shuanghong Shen, Fei Wang, Qi Liu, Enhong Chen, Xin Li
Venue: WWW
Year: 2023

Knowledge Tracing (KT) aims at tracing the evolution of the knowledge states along the learning process of a learner. It has become a crucial task for online learning systems to model the learning process of their users, and further provide their users a personalized learning guidance. However, recent developments in KT based on deep neural networks mostly focus on increasing the accuracy of predicting the next performance of students. We argue that current KT modeling, as well as training paradigm, can lead to models tracing patterns of learner’s learning activities, instead of their evolving knowledge states. In this paper, we propose a new architecture, Diagnostic Transformer (DTransformer), along with a new training paradigm, to tackle this challenge. With DTransformer, we build the architecture from question-level to knowledge-level, explicitly diagnosing learner’s knowledge proficiency from each question mastery states. We also propose a novel training algorithm based on contrastive learning that focuses on maintaining the stability of the knowledge state diagnosis. Through extensive experiments, we will show that with its understanding of knowledge state evolution, DTransformer achieves a better performance prediction accuracy and more stable knowledge state tracing results. We will also show that DTransformer is less sensitive to specific patterns with case study. We open-sourced our code and data at https://github.com/yxonic/DTransformer.
Disentangling Degree-related Biases and Interest for Out-of-Distribution Generalized Directed Network Embedding
Authors: Hyunsik Yoo, Yeon-Chang Lee, Kijung Shin, Sang-Wook Kim
Venue: WWW
Year: 2023

The goal of directed network embedding is to represent the nodes in a given directed network as embeddings that preserve the asymmetric relationships between nodes. While a number of directed network embedding methods have been proposed, we empirically show that the existing methods lack out-of-distribution generalization abilities against degree-related distributional shifts. To mitigate this problem, we propose ODIN (Out-of-Distribution Generalized Directed Network Embedding), a new directed NE method where we model multiple factors in the formation of directed edges. Then, for each node, ODIN learns multiple embeddings, each of which preserves its corresponding factor, by disentangling interest factors and biases related to in- and out-degrees of nodes. Our experiments on four real-world directed networks demonstrate that disentangling multiple factors enables ODIN to yield out-of-distribution generalized embeddings that are consistently effective under various degrees of shifts in degree distributions. Specifically, ODIN universally outperforms 9 state-of-the-art competitors in 2 LP tasks on 4 real-world datasets under both identical distribution (ID) and non-ID settings. The code is available at https://github.com/hsyoo32/odin.