OpenRaiser/Intern-Atlas huggingface dataset:
Hate Speech: Detection, Mitigation and Beyond
Authors: Punyajoy Saha, Mithun Das, Binny Mathew, Animesh Mukherjee
Venue: WSDM
Year: 2023

Social media sites such as Twitter and Facebook have connected billions of people and given the opportunity to the users to share their ideas and opinions instantly. That being said, there are several negative consequences as well such as online harassment, trolling, cyber-bullying, fake news, and hate speech. Out of these, hate speech presents a unique challenge as it is deeply engraved into our society and is often linked with offline violence. Social media platforms rely on human moderators to identify hate speech and take necessary action. However, with the increase in online hate speech, these platforms are turning toward automated hate speech detection and mitigation systems. This shift brings several challenges to the plate, and hence, is an important avenue to explore for the computation social science community. In this tutorial, we present an exposition of hate speech detection and mitigation in three steps. First, we describe the current state of research in the hate speech domain, focusing on different hate speech detection and mitigation systems that have developed over time. Next, we highlight the challenges that these systems might carry like bias and the lack of transparency. The final section concretizes the path ahead, providing clear guidelines for the community working in hate speech and related domains. We also outline the open challenges and research directions for interested researchers.
A Synthetic Search Session Generator for Task-Aware Information Seeking and Retrieval
Authors: Shawon Sarkar, Chirag Shah
Venue: WSDM
Year: 2023

For users working on a complex search task, it is common to address different goals at various stages of the task through query iterations. While addressing these goals, users go through different task states as well. Understanding these task states latent under users' interactions is crucial in identifying users' changing intents and search behaviors to simulate and achieve real-time adaptive search recommendations and retrievals. However, the availability of sizeable real-world web search logs is scarce due to various ethical and privacy concerns, thus often challenging to develop generalizable task-aware computation models. Furthermore, session logs with task state labels are rarer. For many researchers who lack the resources to directly and at scale collect data from users and conduct a time-consuming data annotation process, this becomes a considerable bottleneck to furthering their research. Synthetic search sessions have the potential to address this gap. This paper shares a parsimonious model to simulate synthetic web search sessions with task state information, which interactive information retrieval (IIR) and search personalization studies could utilize to develop and evaluate task-based search and retrieval systems.
Understanding the Effect of Outlier Items in E-commerce Ranking
Authors: Fatemeh Sarvi
Venue: WSDM
Year: 2023

Implicit feedback is an attractive source of training data in Learning to Rank (LTR). However, naively use of this data can produce unfair ranking policies originating from both exogenous and endogenous factors. Exogenous factors comes from biases in the training data, which can lead to rich-get-richer dynamics. Endogenous factors can result in ranking policies that do not allocate exposure among items in a fair way. Item exposure is a common components influencing both endogenous and exogenous factors which depends on not only position but also Inter-item dependencies. In this project, we focus on a specific case of these Inter-item dependencies which is the existence of an outlier in the list. We first define and formalize outlierness in ranking, then study the effects of this phenomenon on endogenous and exogenous factors. We further investigate the visual aspects of presentational features and their impact on item outlierness.
Trustworthy Algorithmic Ranking Systems
Authors: Markus Schedl, Emilia Gómez, Elisabeth Lex
Venue: WSDM
Year: 2023

This tutorial aims at providing its audience an interdisciplinary overview about the topics of fairness and non-discrimination, diversity, and transparency as relevant dimensions of trustworthy AI systems, tailored to algorithmic ranking systems such as search engines and recommender systems. We will equip the mostly technical audience of WSDM with the necessary understanding of the social and ethical implications of their research and development on the one hand, and of recent ethical guidelines and regulatory frameworks addressing the aforementioned dimensions on the other hand. While the tutorial foremost takes a European perspective, starting from the concept of trustworthy AI and discussing EU regulation in this area currently in the implementation stages, we also consider related initiatives worldwide. Since ensuring non-discrimination, diversity, and transparency in retrieval and recommendation systems is an endeavor in which academic institutions and companies in different parts of the world should collaborate, this tutorial is relevant for researchers and practitioners interested in the ethical, social, and legal impact of their work. The tutorial, therefore, targets both academic scholars and practitioners around the globe, by reviewing recent research and providing practical examples addressing these particular trustworthiness aspects, and showcasing how new regulations affect the audience's daily work.
Privacy Aware Experiments without Cookies
Authors: Shiv Shankar, Ritwik Sinha, Saayan Mitra, Viswanathan (Vishy) Swaminathan, Sridhar Mahadevan, Moumita Sinha
Venue: WSDM
Year: 2023

Consider two brands that want to jointly test alternate web experiences for their customers with an A/B test. Such collaborative tests are today enabled usingthird-party cookies, where each brand has information on the identity of visitors to another website, ensuring a consistent treatment experience. With the imminent elimination of third-party cookies, such A/B tests will become untenable. We propose a two-stage experimental design, where the two brands only need to agree on high-level aggregate parameters of the experiment to test the alternate experiences. Our design respects the privacy of customers. We propose an unbiased estimator of the Average Treatment Effect (ATE), and provide a way to use regression adjustment to improve this estimate. On real and simulated data, we show that the approach provides valid estimate of the ATE and is robust to the proportion of visitors overlapping across the brands. Our demonstration describes how a marketer can design such an experiment and analyze the results.
Mining User-aware Multi-relations for Fake News Detection in Large Scale Online Social Networks
Authors: Xing Su, Jian Yang, Jia Wu, Yuchen Zhang
Venue: WSDM
Year: 2023

Users' involvement in creating and propagating news is a vital aspect of fake news detection in online social networks. Intuitively, credible users are more likely to share trustworthy news, while untrusted users have a higher probability of spreading untrustworthy news. In this paper, we construct a dual-layer graph (i.e., news layer and user layer) to extract multi-relations of news and users in social networks to derive rich information for detecting fake news. Based on the dual-layer graph, we propose a fake news detection model Us-DeFake. It learns the propagation features of news in the news layer and the interaction features of users in the user layer. Through the inter-layer in the graph, Us-DeFake fuses the user signals that contain credibility information into the news features, to provide distinctive user-aware embeddings of news for fake news detection. The training process conducts on multiple dual-layer subgraphs obtained by a graph sampler to scale Us-DeFake in large scale social networks. Extensive experiments on real-world datasets illustrate the superiority of Us-DeFake which outperforms all baselines, and the users' credibility signals learned by interaction relation can notably improve the performance of our model.
Compliance Analyses of Australia's Online Household Appliances
Authors: Chang How Tan, Vincent C. S. Lee, Jessie Nghiem, Priya Laxman
Venue: WSDM
Year: 2023

Commercially sold electrical or gas products must comply with the safety standards imposed within a country and get registered and certified by a regulated body. However, with the increasing transition of businesses to e-commerce platforms, it becomes challenging to govern the compliance status of online products. This can increase the risk of purchasing non-compliant products which may be unsafe to use. Additionally, examining the compliance status before purchasing can be strenuous because the relevant compliance information can be ambiguous and not always directly available. Therefore, we collaborated with a regulated body from Australia, Energy Safe Victoria, and conducted compliance analyses for household appliances sold on multiple online platforms. A fully autonomous method shown in this public repository is also introduced to check the compliance status of any online product. In this talk, we discuss the compliance check process, which incorporates fuzzy logic for textual matching and a Convolutional Neural Network (CNN) model to classify the product listing based on the images listed. Subsequently, we studied the results with the business users and found that many online listings are non-compliant, signifying that online-shopping consumers are highly susceptible to buying unsafe products. We hope this talk can inspire more follow-up works that collaborate with regulated bodies to introduce a user-friendly compliance check platform that assists in educating consumers to purchase compliant products.
Bring Your Own View: Graph Neural Networks for Link Prediction with Personalized Subgraph Selection
Authors: Qiaoyu Tan, Xin Zhang, Ninghao Liu, Daochen Zha, Li Li, Rui Chen, Soo-Hyun Choi, Xia Hu
Venue: WSDM
Year: 2023

Graph neural networks (GNNs) have received remarkable success in link prediction (GNNLP) tasks. Existing efforts first predefine the subgraph for the whole dataset and then apply GNNs to encode edge representations by leveraging the neighborhood structure induced by the fixed subgraph. The prominence of GNNLP methods significantly relies on the adhoc subgraph. Since node connectivity in real-world graphs is complex, one shared subgraph is limited for all edges. Thus, the choices of subgraphs should be personalized to different edges. However, performing personalized subgraph selection is nontrivial since the potential selection space grows exponentially to the scale of edges. Besides, the inference edges are not available during training in link prediction scenarios, so the selection process needs to be inductive. To bridge the gap, we introduce a Personalized Subgraph Selector (PS2) as a plug-and-play framework to automatically, personally, and inductively identify optimal subgraphs for different edges when performing GNNLP. PS2 is instantiated as a bi-level optimization problem that can be efficiently solved differently. Coupling GNNLP models with PS2, we suggest a brand-new angle towards GNNLP training: by first identifying the optimal subgraphs for edges; and then focusing on training the inference model by using the sampled subgraphs. Comprehensive experiments endorse the effectiveness of our proposed method across various GNNLP backbones (GCN, GraphSage, NGCF, LightGCN, and SEAL) and diverse benchmarks (Planetoid, OGB, and Recommendation datasets). Our code is publicly available at https://github.com/qiaoyu-tan/PS2
Data-Efficient Graph Learning Meets Ethical Challenges
Authors: Tao Tang
Venue: WSDM
Year: 2023

Recommender systems have achieved great success in our daily life. In recent years, the ethical concerns of AI systems have gained lots of attention. At the same time, graph learning techniques are powerful in modelling the complex relations among users and items under recommender system applications. These graph learning- based methods are data hungry, which brought a significant data efficiency challenge. In this proposal, I introduce my PhD research from three aspects: 1) Efficient privacy-preserving recommendation for imbalanced data. 2) Efficient recommendation model training for Insufficient samples. 3) Explainability in the social recommendation. Challenges and solutions of the above research problems have been proposed in this proposal.
AutoML for Deep Recommender Systems: Fundamentals and Advances
Authors: Ruiming Tang, Bo Chen, Yejing Wang, Huifeng Guo, Yong Liu, Wenqi Fan, Xiangyu Zhao
Venue: WSDM
Year: 2023

Recommender systems have become increasingly important in our daily lives since they play an important role in mitigating the information overload problem, especially in many user-oriented online services. Recommender systems aim to identify a set of items that best match users' explicit or implicit preferences, by utilizing the user and item interactions to improve the accuracy. With the fast advancement of deep neural networks (DNNs) in the past few decades, recommendation techniques have achieved promising performance. However, we still meet three inherent challenges to design deep recommender systems (DRS): 1) the majority of existing DRS are developed based on hand-crafted components, which requires ample expert knowledge recommender systems; 2) human error and bias can lead to suboptimal components, which reduces the recommendation effectiveness; 3) non-trivial time and engineering efforts are usually required to design the task-specific components in different recommendation scenarios. In this tutorial, we aim to give a comprehensive survey on the recent progress of advanced Automated Machine Learning (AutoML) techniques for solving the above problems in deep recommender systems. More specifically, we will present feature selection, feature embedding search, feature interaction search, and whole DRS pipeline model training and comprehensive search for deep recommender systems. In this way, we expect academic researchers and industrial practitioners in related fields can get deep understanding and accurate insight into the spaces, stimulate more ideas and discussions, and promote developments of technologies in recommendations.
Graph Neural Networks with Interlayer Feature Representation for Image Super-Resolution
Authors: Shenggui Tang, Kaixuan Yao, Jianqing Liang, Zhiqiang Wang, Jiye Liang
Venue: WSDM
Year: 2023

Although deep learning has been extensively studied and achieved remarkable performance on single image super-resolution (SISR), existing convolutional neural networks (CNN) mainly focus on broader and deeper architecture design, ignoring the detailed information of the image itself and the potential relationship between the features. Recently, several attempts have been made to address the SISR with graph representation learning. However, existing GNN-based methods learning to deal with the SISR problem are limited to the information processing of the entire image or the relationship processing between different feature images of the same layer, ignoring the interdependence between the extracted features of different layers, which is not conducive to extracting deeper hierarchical features. In this paper, we propose an interlayer feature representation based graph neural network for image super-resolution (LSGNN), which consists of a layer feature graph representation learning module and a channel spatial attention module. The layer feature graph representation learning module mainly captures the interdependence between the features of different layers, which can learn more fine-grained image detail features. In addition, we also unified a channel attention module and a spatial attention module into our model, which takes into account the channel dimension information and spatial scale information, to improve the expressive ability, and achieve high quality image details. Extensive experiments and ablation studies demonstrate the superiority of the proposed model.
Range Restricted Route Recommendation Based on Spatial Keyword
Authors: Hongwei Tang, Detian Zhang
Venue: WSDM
Year: 2023

In this paper, we focus on a new route recommendation problem, i.e., when a user gives a keyword and range constraint, the route that contains the maximum number of POIs tagged with the keyword or similar POIs in the range will be returned for him. This is a practical problem when people want to explore a place, e.g., find a route within 2 km containing as many clothing stores as possible. To solve the problem, we first calculate the score of each edge in road networks based on the number and similarity of POIs. Then, we reformulate the problem into finding the path in a graph with the maximum score within the distance constraint problem, which is proved NP-hard. Given this, we not only propose an exact branch and bound (BnB) algorithm, but also devise a more efficient top-k based network expansion (k-NE) algorithm to find the near-optimal solution. Extensive experiments on real datasets not only verify the effectiveness of the proposed route recommendation algorithm, but also show that the efficiency and accuracy of k-NE algorithm are completely acceptable.
Social Public Health Infrastructure for a Smart City Citizen Patient: Advances and Opportunities for AI Driven Disruptive Innovation
Authors: Ankur Teredesai
Venue: WSDM
Year: 2023

Promoting health, preventing disease, and prolonging life are central to the success of any smart city initiative. Today, wireless communication, data infrastructure, and low-cost sensors such as lifestyle and activity trackers are making it increasingly possible for cities to collect, collate, and innovate on developing a smart infrastructure. Combining this with AI driven disruptions for human behavior change can fundamentally transform delivery of public health for the citizen patient[4]. Most urban development government bodies consider such infrastructure to be a distributed ecosystem consisting of physical infrastructure, institutional infrastructure, social infrastructure and economic infrastructure[1]. In this talk we will focus mostly on the social health infrastructure component and first showcase some of the recent initiatives created with the purpose of addressing health which is a key social goal, as a smart city goal. Specifically we will discuss how technical advances in IoT, recommendation systems, geospatial computing, and digital health therapeutics are creating a new future. Yet, such advances are not addressing the issues of making healthy behavior change sustainable with broad health equity for those that need it most[3]. Overwhelming nature of siloed apps and digital health solutions which often leave the citizens overwhelmed and those most in need underserved[2]. This talk highlights the advances and opportunities created when behavioral economics and public health combine with AI and cloud infrastructure to make smart public health initiatives personalized to each individual citizen patient.
Concept-Oriented Transformers for Visual Sentiment Analysis
Authors: Quoc-Tuan Truong, Hady W. Lauw
Venue: WSDM
Year: 2023

In the richly multimedia Web, detecting sentiment signals expressed in images would support multiple applications, e.g., measuring customer satisfaction from online reviews, analyzing trends and opinions from social media. Given an image, visual sentiment analysis aims at recognizing positive or negative sentiment, and occasionally neutral sentiment as well. A nascent yet promising direction is Transformer-based models applied to image data, whereby Vision Transformer (ViT) establishes remarkable performance on large-scale vision benchmarks. In addition to investigating the fitness of ViT for visual sentiment analysis, we further incorporate concept orientation into the self-attention mechanism, which is the core component of Transformer. The proposed model captures the relationships between image features and specific concepts. We conduct extensive experiments on Visual Sentiment Ontology (VSO) and Yelp.com online review datasets, showing that not only does the proposed model significantly improve upon the base model ViT in detecting visual sentiment but it also outperforms previous visual sentiment analysis models with narrowly-defined orientations. Additional analyses yield insightful results and better understanding of the concept-oriented self-attention mechanism.
Variance-Minimizing Augmentation Logging for Counterfactual Evaluation in Contextual Bandits
Authors: Aaron David Tucker, Thorsten Joachims
Venue: WSDM
Year: 2023

Methods for offline A/B testing and counterfactual learning are seeing rapid adoption in search and recommender systems, since they allow efficient reuse of existing log data. However, there are fundamental limits to using existing log data alone, since the counterfactual estimators that are commonly used in these methods can have large bias and large variance when the logging policy is very different from the target policy being evaluated. To overcome this limitation, we explore the question of how to design data-gathering policies that most effectively augment an existing dataset of bandit feedback with additional observations for both learning and evaluation. To this effect, this paper introduces Minimum Variance Augmentation Logging (MVAL), a method for constructing logging policies that minimize the variance of the downstream evaluation or learning problem. We explore multiple approaches to computing MVAL policies efficiently, and find that they can be substantially more effective in decreasing the variance of an estimator than naïve approaches.
4th Crowd Science Workshop - CANDLE: Collaboration of Humans and Learning Algorithms for Data Labeling
Authors: Dmitry Ustalov, Saiph Savage, Niels van Berkel, Yang Liu
Venue: WSDM
Year: 2023

Crowdsourcing has been used to produce impactful and large-scale datasets for Machine Learning and Artificial Intelligence (AI), such as ImageNET, SuperGLUE, etc. Since the rise of crowdsourcing in early 2000s, the AI community has been studying its computational, system design, and data-centric aspects at various angles. We welcome the studies on developing and enhancing of crowdworker-centric tools, that offer task matching, requester assessment, instruction validation, among other topics. We are also interested in exploring methods that leverage the integration of crowdworkers to improve the recognition and performance of the machine learning models. Thus, we invite studies that focus on shipping active learning techniques, methods for joint learning from noisy data and from crowds, novel approaches for crowd-computer interaction, repetitive task automation, and role separation between humans and machines. Moreover, we invite works on designing and applying such techniques in various domains, including e-commerce and medicine.