OpenRaiser/Intern-Atlas huggingface dataset:
Toward Open-domain Slot Filling via Self-supervised Co-training
Authors: Adib Mosharrof, Moghis Fereidouni, A. B. Siddique
Venue: WWW
Year: 2023

Slot filling is one of the critical tasks in modern conversational systems. The majority of existing literature employs supervised learning methods, which require labeled training data for each new domain. Zero-shot learning and weak supervision approaches, among others, have shown promise as alternatives to manual labeling. Nonetheless, these learning paradigms are significantly inferior to supervised learning approaches in terms of performance. To minimize this performance gap and demonstrate the possibility of open-domain slot filling, we propose a Self-supervised Co-training framework, called , that requires zero in-domain manually labeled training examples and works in three phases. Phase one acquires two sets of complementary pseudo labels automatically. Phase two leverages the power of the pre-trained language model BERT, by adapting it for the slot filling task using these sets of pseudo labels. In phase three, we introduce a self-supervised co-training mechanism, where both models automatically select high-confidence soft labels to further improve the performance of the other in an iterative fashion. Our thorough evaluations show that outperforms state-of-the-art models by 45.57% and 37.56% on SGD and MultiWoZ datasets, respectively. Moreover, our proposed framework achieves comparable performance when compared to state-of-the-art fully supervised models.
Personalised Search in E-Comm Groceries
Authors: Ramprabhu Murugesan, Anuja Sharan
Venue: WWW
Year: 2023

Personalized Search(henceforth called P10d Search) focuses to deliver user-specific search results based on the previous purchases. Search engine retrieves the result based on the defined relevancy algorithm. When a user searches a keyword, search engine constructs the search query based on the defined searchable fields/attributes along with configured relevancy algorithm. Position of the item retrieved in search results is determined by the search algorithm based on the search term. The results are further refined or ranked based on different click stream signals, product features, market data to provide much relevant results. Personalisation provides the ranked the list of items for a given user based on past purchases. Personalisation is agnostic of search query and takes user id, cart additions, site taxonomy and user’s shopping history as input signals. In summary, search engine queries data based on relevancy and personalisation engine retrieves based purely on purchases. Goal of personalised search is to enhance the search results by adding personalised results without affecting the search relevance.
Multi-turn mediated solutions for Conversational Artificial Intelligent systems leveraging graph-based techniques
Authors: Riya Naik
Venue: WWW
Year: 2023

The current era is dominated by intelligent Question Answering (QA) systems that can instantly answer almost all their questions, saving users search time and increasing the throughput and precision in the applied domain. A vast amount of work is being carried out in QA systems to deliver better content satisfying users’ information needs [2]. Since QA systems are ascending the cycle of emerging technologies, there are potential research gaps that can be explored. QA systems form a significant part of Conversational Artificial Intelligent systems giving rise to a new research pathway, i.e., Conversational Question Answering (CQA) systems [32]. We propose to design and develop a CQA system leveraging Hypergraph-based techniques. The approach focuses on the multi-turn conversation and multi-context to gauge users’ exact information needs and deliver better answers. We further aim to address "supporting evidence-based retrieval" for fact-based responsible answer generation. Since the QA system requires a large amount of data and processing, we also intend to investigate hardware performance for effective system utilization.
Investigating Action-Space Generalization in Reinforcement Learning for Recommendation Systems
Authors: Abhishek Naik, Bo Chang, Alexandros Karatzoglou, Martin Mladenov, Ed H. Chi, Minmin Chen
Venue: WWW
Year: 2023

Recommender systems are used to suggest items to users based on the users’ preferences. Such systems often deal with massive item sets and incredibly sparse user-item interactions, which makes it very challenging to generate high-quality personalized recommendations. Reinforcement learning (RL) is a framework for sequential decision making and naturally formulates recommender-system tasks: recommending items as actions in different user and context states to maximize long-term user experience. We investigate two RL policy parameterizations that generalize sparse user-items interactions by leveraging the relationships between actions: parameterizing the policy over action features as a softmax or Gaussian distribution. Our experiments on synthetic problems suggest that the Gaussian parameterization—which is not commonly used on recommendation tasks—is more robust to the set of action features than the softmax parameterization. Based on these promising results, we propose a more thorough investigation of the theoretical properties and empirical benefits of the Gaussian parameterization for recommender systems.
Graph-Based Hierarchical Attention Network for Suicide Risk Detection on Social Media
Authors: Usman Naseem, Jinman Kim, Matloob Khushi, Adam G. Dunn
Venue: WWW
Year: 2023

The widespread use of social media for expressing personal thoughts and emotions makes it a valuable resource for identifying individuals at risk of suicide. Existing sequential learning-based methods have shown promising results. However, these methods may fail to capture global features. Due to its inherent ability to learn interconnected data, graph-based methods can address this gap. In this paper, we present a new graph-based hierarchical attention network (GHAN) that uses a graph convolutional neural network with an ordinal loss to improve suicide risk identification on social media. Specifically, GHAN first captures global features by constructing three graphs to capture semantic, syntactic, and sequential contextual information. Then encoded textual features are fed to attentive transformers’ encoder and optimized to factor in the increasing suicide risk levels using an ordinal classification layer hierarchically for suicide risk detection. Experimental results show that the proposed GHAN outperformed state-of-the-art methods on a public Reddit dataset.
Katti: An Extensive and Scalable Tool for Website Analyses
Authors: Florian Nettersheim, Stephan Arlt, Michael Rademacher, Florian Dehling
Venue: WWW
Year: 2023

Research on web security and privacy frequently relies on tools that analyze a set of websites. One major obstacle to the judicious analysis is the employment of a rock-solid and feature-rich web crawler. For example, the automated analysis of ad-malware campaigns on websites requests crawling a vast set of domains on multiple real web browsers, while simultaneously mitigating bot detections and applying user interactions on websites. Further, the ability to attach various threat analysis frameworks lacks current tooling efforts in web crawling and analyses. In this paper we introduce Katti, which overcomes several of today’s technical hurdles in web crawling. Our tool employs a distributed task queue that efficiently and reliably handles both large crawling and threat analyses requests. Katti extensively collects all available web data through an integrated person-in-the-middle proxy. Moreover, Katti is not limited to a specific use case, allowing users to easily customize our tool to their individual research intends.
Link Prediction on Latent Heterogeneous Graphs
Authors: Trung-Kien Nguyen, Zemin Liu, Yuan Fang
Venue: WWW
Year: 2023

On graph data, the multitude of node or edge types gives rise to heterogeneous information networks (HINs). To preserve the heterogeneous semantics on HINs, the rich node/edge types become a cornerstone of HIN representation learning. However, in real-world scenarios, type information is often noisy, missing or inaccessible. Assuming no type information is given, we define a so-called latent heterogeneous graph (LHG), which carries latent heterogeneous semantics as the node/edge types cannot be observed. In this paper, we study the challenging and unexplored problem of link prediction on an LHG. As existing approaches depend heavily on type-based information, they are suboptimal or even inapplicable on LHGs. To address the absence of type information, we propose a model named LHGNN, based on the novel idea of semantic embedding at node and path levels, to capture latent semantics on and between nodes. We further design a personalization function to modulate the heterogeneous contexts conditioned on their latent semantics w.r.t. the target node, to enable finer-grained aggregation. Finally, we conduct extensive experiments on four benchmark datasets, and demonstrate the superior performance of LHGNN.
Learning Faithful Attention for Interpretable Classification of Crisis-Related Microblogs under Constrained Human Budget
Authors: Thi Huyen Nguyen, Koustav Rudra
Venue: WWW
Year: 2023

The recent widespread use of social media platforms has created convenient ways to obtain and spread up-to-date information during crisis events such as disasters. Time-critical analysis of crisis data can help human organizations gain actionable information and plan for aid responses. Many existing studies have proposed methods to identify informative messages and categorize them into different humanitarian classes. Advanced neural network architectures tend to achieve state-of-the-art performance, but the model decisions are opaque. While attention heatmaps show insights into the model’s prediction, some studies found that standard attention does not provide meaningful explanations. Alternatively, recent works proposed interpretable approaches for the classification of crisis events that rely on human rationales to train and extract short snippets as explanations. However, the rationale annotations are not always available, especially in real-time situations for new tasks and events. In this paper, we propose a two-stage approach to learn the rationales under minimal human supervision and derive faithful machine attention. Extensive experiments over four crisis events show that our model is able to obtain better or comparable classification performance (∼ 86% Macro-F1) to baselines and faithful attention heatmaps using only 40-50% human-level supervision. Further, we employ a zero-shot learning setup to detect actionable tweets along with actionable word snippets as rationales.
Ad Auction Design with Coupon-Dependent Conversion Rate in the Auto-bidding World
Authors: Bonan Ni, Xun Wang, Qi Zhang, Pingzhong Tang, Zhourong Chen, Tianjiu Yin, Liangni Lu, Xiaobing Liu, Kewu Sun, Zhe Ma
Venue: WWW
Year: 2023

Online advertising has become a dominant source of revenue of the Internet. In classic auction theory, only the auctioneer (i.e., the platform) and buyers (i.e., the advertisers) are involved, while the advertising audiences are ignored. For ecommerce advertising, however, the platform can provide coupons for the advertising audiences and nudge them into purchasing more products at lower prices (e.g., 2 dollars off the regular price). Such promotions can lead to an increase in amount and value of purchases. In this paper, we jointly design the coupon value computation, slot allocation, and payment of online advertising in an auto-bidding world. Firstly, we propose the auction mechanism, named CFA-auction (i.e., Coupon-For-the-Audiences-auction), which takes advertising audiences into account in the auction design. We prove the existence of pacing equilibrium, and show that CFA-auction satisfies the IC (incentive compatibility), IR (individual rationality) constraints. Then, we study the optimality of CFA-auction, and prove it can maintain an approximation of the optimal. Finally, experimental evaluation results on both offline dataset as well as online A/B test demonstrate the effectiveness of CFA-auction.
Locating Faulty Applications via Semantic and Topology Estimation
Authors: Shuyi Niu, Jiawei Jin, Xiutian Huang, Yonggeng Wang, Wenhao Xu, Youyong Kong
Venue: WWW
Year: 2023

With the explosion of Internet product users, how to locate the faulty ones from numerous back-end applications after a customer complaint has become an essential issue in improving user experience. However, existing solutions mostly rely on manual testing to infer the fault, severely limiting their efficiency. In this paper, we transform the problem of locating faulty applications into two subproblems and propose a fully automated framework. We design a scorecard model in one stage to evaluate the semantic relevance between applications and customer complaints. Then in the other stage, topology graphs that reflect the actual calling relationship and engineering connection relationship between applications are utilized to evaluate the topology relevance between applications. Specifically, we employ a multi-graph co-learning framework constrained by consistency-independence loss and an engineering-theory-driven clustering strategy for the unsupervised learning of graphs. With semantic and topology relevance, we can comprehensively locate relevant faulty applications. Experiments on the Alipay dataset show that our method gains significant improvements in both model performance and efficiency.
On Cohesively Polarized Communities in Signed Networks
Authors: Jason Niu, Ahmet Erdem Sariyüce
Venue: WWW
Year: 2023

Locating and characterizing polarization is one of the most important issues to enable a healthier web ecosystem. Finding groups of nodes that form strongly stable agreements and participate in collective conflicts with other groups is an important problem in this context. Previous works approach this problem by finding balanced subgraphs, in which the polarity measure is optimized, that result in large subgraphs without a clear notion of agreement or conflict. In real-world signed networks, balanced subgraphs are often not polarized as in the case of a subgraph with only positive edges. To remedy this issue, we leverage the notion of cohesion — we find pairs of cohesively polarized communities where each node in a community is positively connected to nodes in the same community and negatively connected to nodes in the other community. To capture the cohesion along with the polarization, we define a new measure, dichotomy. We leverage the balanced triangles, which model the cohesion and polarization at the same time, to design a heuristic that results in good seedbeds for polarized communities in real-world signed networks. Then, we introduce the electron decomposition which finds cohesively polarized communities with high dichotomy score. In an extensive experimental evaluation, we show that our method finds cohesively polarized communities and outperforms the state-of-the-art methods with respect to several measures. Moreover, our algorithm is more efficient than the existing methods and practical for large-scale networks.
AppSniffer: Towards Robust Mobile App Fingerprinting Against VPN
Authors: Sanghak Oh, Minwook Lee, Hyunwoo Lee, Elisa Bertino, Hyoungshick Kim
Venue: WWW
Year: 2023

Application fingerprinting is a useful data analysis technique for network administrators, marketing agencies, and security analysts. For example, an administrator can adopt application fingerprinting techniques to determine whether a user’s network access is allowed. Several mobile application fingerprinting techniques (e.g., FlowPrint, AppScanner, and ET-BERT) were recently introduced to identify applications using the characteristics of network traffic. However, we find that the performance of the existing mobile application fingerprinting systems significantly degrades when a virtual private network (VPN) is used. To address such a shortcoming, we propose a framework dubbed AppSniffer that uses a two-stage classification process for mobile app fingerprinting. In the first stage, we distinguish VPN traffic from normal traffic; in the second stage, we use the optimal model for each traffic type. Specifically, we propose a stacked ensemble model using Light Gradient Boosting Machine (LightGBM) and a FastAI library-based neural network model to identify applications’ traffic when a VPN is used. To show the feasibility of AppSniffer, we evaluate the detection accuracy of AppSniffer for 150 popularly used Android apps. Our experimental results show that AppSniffer effectively identifies mobile applications over VPNs with F1-scores between 84.66% and 95.49% across four different VPN protocols. In contrast, the best state-of-the-art method (i.e., AppScanner) demonstrates significantly lower F1-scores between 25.63% and 47.56% in the same settings. Overall, when normal traffic and VPN traffic are mixed, AppSniffer achieves an F1-score of 90.63%, which is significantly better than AppScanner that shows an F1-score of 70.36%.
Workshops at the Web Conference 2023
Authors: Femke Ongenae, Riccardo Tommasini
Venue: WWW
Year: 2023

This paper introduces the 20 workshops that were organized at The Web Conference 2023.
Search Personalization at Netflix
Authors: Vito Ostuni, Christoph Kofler, Manjesh Nilange, Sudarshan Lamkhede, Dan Zylberglejd
Venue: WWW
Year: 2023

At Netflix, personalization plays a key role in several aspects of our user experience, from ranking titles to constructing an optimal Homepage. Although personalization is a well established research field, its application to search presents unique problems and opportunities. In this paper, we describe the evolution of Search personalization at Netflix, its unique challenges, and provide a high level overview of relevant solutions.
EmpMFF: A Multi-factor Sequence Fusion Framework for Empathetic Response Generation
Authors: Xiaobing Pang, Yequan Wang, Siqi Fan, Lisi Chen, Shuo Shang, Peng Han
Venue: WWW
Year: 2023

Empathy is one of the fundamental abilities of dialog systems. In order to build more intelligent dialogue systems, it’s important to learn how to demonstrate empathy toward others. Existing studies focus on identifying and leveraging the user’s coarse emotion to generate empathetic responses. However, human emotion and dialog act (e.g., intent) evolve as the talk goes along in an empathetic dialogue. This leads to the generated responses with very different intents from the human responses. As a result, empathy failure is ultimately caused. Therefore, using fine-grained emotion and intent sequential data on conversational emotions and dialog act is crucial for empathetic response generation. On the other hand, existing empathy models overvalue the empathy of responses while ignoring contextual relevance, which results in repetitive model-generated responses. To address these issues, we propose a Multi-Factor sequence Fusion framework (EmpMFF) based on conditional variational autoencoder. To generate empathetic responses, the proposed EmpMFF encodes a combination of contextual, emotion, and intent information into a continuous latent variable, which is then fed into the decoder. Experiments on the EmpatheticDialogues benchmark dataset demonstrate that EmpMFF exhibits exceptional performance in both automatic and human evaluations.
Who Funds Misinformation? A Systematic Analysis of the Ad-related Profit Routines of Fake News Sites
Authors: Emmanouil Papadogiannakis, Panagiotis Papadopoulos, Evangelos P. Markatos, Nicolas Kourtellis
Venue: WWW
Year: 2023

Fake news is an age-old phenomenon, widely assumed to be associated with political propaganda published to sway public opinion. Yet, with the growth of social media, it has become a lucrative business for Web publishers. Despite many studies performed and countermeasures proposed, unreliable news sites have increased in the last years their share of engagement among the top performing news sources. Stifling fake news impact depends on our efforts in limiting the (economic) incentives of fake news producers. In this paper, we aim at enhancing the transparency around these exact incentives, and explore: Who supports the existence of fake news websites via paid ads, either as an advertiser or an ad seller? Who owns these websites and what other Web business are they into? We are the first to systematize the auditing process of fake news revenue flows. We identify the companies that advertise in fake news websites and the intermediary companies responsible for facilitating those ad revenues. We study more than 2,400 popular news websites and show that well-known ad networks, such as Google and IndexExchange, have a direct advertising relation with more than 40% of fake news websites. Using a graph clustering approach on 114.5K sites, we show that entities who own fake news sites, also operate other types of websites pointing to the fact that owning a fake news website is part of a broader business operation.
Towards Automated Detection of Risky Images Shared by Youth on Social Media
Authors: Jinkyung Park, Joshua Gracie, Ashwaq Alsoubai, Gianluca Stringhini, Vivek K. Singh, Pamela J. Wisniewski
Venue: WWW
Year: 2023

With the growing ubiquity of the Internet and access to media-based social media platforms, the risks associated with media content sharing on social media and the need for safety measures against such risks have grown paramount. At the same time, risk is highly contextualized, especially when it comes to media content youth share privately on social media. In this work, we conducted qualitative content analyses on risky media content flagged by youth participants and research assistants of similar ages to explore contextual dimensions of youth online risks. The contextual risk dimensions were then used to inform semi- and self-supervised state-of-the-art vision transformers to automate the process of identifying risky images shared by youth. We found that vision transformers are capable of learning complex image features for use in automated risk detection and classification. The results of our study serve as a foundation for designing contextualized and youth-centered machine-learning methods for automated online risk detection.
Trust the Process: Analyzing Prospective Provenance for Data Cleaning
Authors: Nikolaus Nova Parulian, Bertram Ludäscher
Venue: WWW
Year: 2023

In the field of data-driven research and analysis, the quality of results largely depends on the quality of the data used. Data cleaning is a crucial step in improving the quality of data. Still, it is equally important to document the steps made during the data cleaning process to ensure transparency and enable others to assess the quality of the resulting data. While provenance models such as W3C PROV have been introduced to track changes and events related to any entity, their use in documenting the provenance of data-cleaning workflows can be challenging, particularly when mixing different types of documents or entities in the model. To address this, we propose a conceptual model and analysis that breaks down data-cleaning workflows into process abstraction and workflow recipes, refining operations to the column level. This approach provides users with detailed provenance information, enabling transparency, auditing, and support for data cleaning workflow improvements. Our model has several features that allow static analysis, e.g., to determine the minimal input schema and expected output schema for running a recipe, to identify which steps violate the column schema requirement constraint, and to assess the reusability of a recipe on a new dataset. We hope that our model and analysis will contribute to making data processing more transparent, accessible, and reusable.