OpenRaiser/Intern-Atlas huggingface dataset:
A Deep Markov Model for Clickstream Analytics in Online Shopping
Authors: Yilmazcan Özyurt, Tobias Hatt, Ce Zhang, Stefan Feuerriegel
Venue: WWW
Year: 2022

Machine learning is widely used in e-commerce to analyze clickstream sessions and then to allocate marketing resources. Traditional neural learning can model long-term dependencies in clickstream data, yet it ignores the different shopping phases (i. e., goal-directed search vs. browsing) in user behavior as theorized by marketing research. In this paper, we develop a novel, theory-informed machine learning model to account for different shopping phases as defined in marketing theory. Specifically, we formalize a tailored attentive deep Markov model called ClickstreamDMM for predicting the risk of user exits without purchase in e-commerce web sessions. Our ClickstreamDMM combines (1) an attention network to learn long-term dependencies in clickstream data and (2) a latent variable model to capture different shopping phases (i. e., goal-directed search vs. browsing). Due to the interpretable structure, our ClickstreamDMM allows marketers to generate new insights on how shopping phases relate to actual purchase behavior. We evaluate our model using real-world clickstream data from a leading e-commerce platform consisting of 26,279 sessions with 250,287 page clicks. Thereby, we demonstrate that our model is effective in predicting user exits without purchase: compared to existing baselines, it achieves an improvement by 11.5 % in AUROC and 12.7 % in AUPRC. Overall, our model enables e-commerce platforms to detect users at the risk of exiting without purchase. Based on it, e-commerce platforms can then intervene with marketing resources to steer users toward purchasing.
Unsupervised Customer Segmentation with Knowledge Graph Embeddings
Authors: Sumit Pai, Fiona Brennan, Adrianna Janik, Teutly Correia, Luca Costabello
Venue: WWW
Year: 2022

We propose an unsupervised customer segmentation method from behavioral data. We model sequences of beer consumption from a publicly available dataset of 2.9M reviews of more than 110,000 brands over 12 years as a knowledge graph, learn their representations with knowledge graph embedding models, and apply off-the-shelf cluster analysis. Experiments and clusters interpretation show that we learn meaningful clusters of beer customers, without relying on expensive consumer surveys or time-consuming data annotation campaigns.
PNMTA: A Pretrained Network Modulation and Task Adaptation Approach for User Cold-Start Recommendation
Authors: Haoyu Pang, Fausto Giunchiglia, Ximing Li, Renchu Guan, Xiaoyue Feng
Venue: WWW
Year: 2022

User cold-start recommendation is a serious problem that limits the performance of recommender systems (RSs). Recent studies have focused on treating this issue as a few-shot problem and seeking solutions with model-agnostic meta-learning (MAML). Such methods regard making recommendations for one user as a task and adapt to new users with a few steps of gradient updates on the meta-model. However, none of those methods consider the limitation of user representation learning imposed by the special task setting of MAML-based RSs. And they learn a common meta-model for all users while ignoring the implicit grouping distribution induced by the correlation differences among users. In response to the above problems, we propose a pretrained network modulation and task adaptation approach (PNMTA) for user cold-start recommendation. In the pretraining stage, a pretrained model is obtained with non-meta-learning methods to achieve better user representation and generalization, which can also transfer the learned knowledge to the meta-learning stage for modulation. During the meta-learning stage, an encoder modulator is utilized to realize the memorization and correction of prior parameters for the meta-learning task, and a predictor modulator is introduced to condition the model initialization on the task identity for adaptation steps. In addition, PNMTA can also make use of the existing non-cold-start users for pretraining. Comprehensive experiments on two benchmark datasets demonstrate that our model can achieve significant and consistent improvements against other state-of-the-art methods.
Building a Public Domain Voice Database for Odia
Authors: Subhashish Panigrahi
Venue: WWW
Year: 2022

Projects like Mozilla Common Voice were born to address the challenges of unavailability of voice data or the high cost of available data for use in speech technology such as Automatic Speech Recognition (ASR) research and application development. The pilot detailed in this paper is about creating a large freely-licensed public repository of transcribed speech in the Odia language as such a repository was not known to be available. The strategy and methodology behind this process are based on the OpenSpeaks project. Licensed under a Public Domain Dedication (CC0 1.0), the repository currently includes audio recordings of pronunciations for more than 55,000 unique words in Odia, including more than 5,600 recordings of words in the northern Odia dialect Baleswari. No known public listing of words in this dialect was found by the author prior to this pilot. This repository is arguably the most extensive transcribed speech corpus in Odia that is also available publicly under any free and open license. This paper details the strategy, approach, and process behind building both the text and the speech corpus using many open source tools such as Lingua Libre, which can be helpful in building text and speech data for different low-medium-resource languages.
Leveraging Google's Publisher-Specific IDs to Detect Website Administration
Authors: Emmanouil Papadogiannakis, Panagiotis Papadopoulos, Evangelos P. Markatos, Nicolas Kourtellis
Venue: WWW
Year: 2022

Digital advertising is the most popular way for content monetization on the Internet. Publishers spawn new websites, and older ones change hands with the sole purpose of monetizing user traffic. In this ever-evolving ecosystem, it is challenging to effectively answer questions such as: Which entities monetize what websites? What categories of websites does an average entity typically monetize on and how diverse are these websites? How has this website administration ecosystem changed across time? In this paper, we propose a novel, graph-based methodology to detect administration of websites on the Web, by exploiting the ad-related publisher-specific IDs. We apply our methodology across the top 1 million websites and study the characteristics of the created graphs of website administration. Our findings show that approximately 90% of the websites are associated each with a single publisher, and that small publishers tend to manage less popular websites. We perform a historical analysis of up to 8 million websites, and find a new, constantly rising number of (intermediary) publishers that control and monetize traffic from hundreds of websites, seeking a share of the ad-market pie. We also observe that over time, websites tend to move from big to smaller administrators.
The Impact of Twitter Labels on Misinformation Spread and User Engagement: Lessons from Trump's Election Tweets
Authors: Orestis Papakyriakopoulos, Ellen P. Goodmann
Venue: WWW
Year: 2022

Social media platforms are performing “soft moderation” by attaching warning labels to misinformation to reduce dissemination of, and engagement with, such content. This study investigates the warning labels that Twitter placed on Donald Trump’s false tweets about the 2020 US Presidential election. It specifically studies their relation to misinformation spread, and the magnitude and nature of user engagement. We categorize the warning labels by type –“veracity labels” calling out falsity and “contextual labels” providing more information. In addition, we categorize labels by their rebuttal strength and textual overlap (linguistic, topical) with the underlying tweet. We look at user interactions (liking, retweeting, quote tweeting, and replying), the content of user replies, and the type of user involved (partisanship and Twitter activity level) according to various standard metrics. Using appropriate statistical tools, we find that, overall, label placement did not change the propensity of users to share and engage with labeled content, but the falsity of content did. However, we show that the presence of textual overlap in labels did reduce user interactions, while stronger rebuttals reduced the toxicity in comments. We also find that users were more likely to discuss their positions on the underlying tweets in replies when the labels contained rebuttals. When false content was labeled, results show that liberals engaged more than conservatives. Labels also increased the engagement of more passive Twitter users. This case study has direct implications for the design of effective soft moderation and related policies.
A Framework to Enhance Smart Citizen Science in Coastal Areas
Authors: Maria Papoutsoglou, Konstantinos Markakis, Loukas Chatzivasili, Georgia M. Kapitsaki, Kostas Magoutis, Leonidas Katelaris, Chryssoula Bekiari
Venue: WWW
Year: 2022

Life quality in a city can be affected by the way citizens interact with the city. Under a smart city concept, citizens are acting as human sensors reporting natural hazards, generating real-time data and enhancing awareness about environmental issues. This crowdsourcing knowledge supports the city’s sustainability and tourism. Specifically, smart seaside cities can fully utilize citizen science data to improve the efficiency of city services, such as smart tourism, smart transportation etc. Environmental assistance and awareness is a beach monitoring issue that could be enhanced through crowdsourcing knowledge. Especially for coastal areas which are under the Natura 2000 network and are characterized as blue flag seas, it is important to identify and map citizens’ knowledge. To facilitate this, we introduce a novel framework aimed at: i) utilizing biodiversity data from open source platforms and organizational observations, ii) collecting the knowledge generated from citizens, iii) enhancing citizens’ awareness, and iv) reporting environmental issues in their city’s coastal areas. The proposed framework exploits these aspects and through the creation of a novel knowledge platform, it aims to provide geospatial, collective awareness applications as an output to support the sustainability of smart coastal spaces.
Fairness Audit of Machine Learning Models with Confidential Computing
Authors: Saerom Park, Seongmin Kim, Yeon-sup Lim
Venue: WWW
Year: 2022

Algorithmic discrimination is one of the significant concerns in applying machine learning models to a real-world system. Many researchers have focused on developing fair machine learning algorithms without discrimination based on legally protected attributes. However, the existing research has barely explored various security issues that can occur while evaluating model fairness and verifying fair models. In this study, we propose a fairness audit framework that assesses the fairness of ML algorithms while addressing potential security issues such as data privacy, model secrecy, and trustworthiness. To this end, our proposed framework utilizes confidential computing and builds a chain of trust through enclave attestation primitives combined with public scrutiny and state-of-the-art software-based security techniques, enabling fair ML models to be securely certified and clients to verify a certified one. Our micro-benchmarks on various ML models and real-world datasets show the feasibility of the fairness certification implemented with Intel SGX in practice. In addition, we analyze the impact of data poisoning, which is an additional threat during data collection for fairness auditing. Based on the analysis, we illustrate the theoretical curves of fairness gap and minimal group size and the empirical results of fairness certification on poisoned datasets.
CGC: Contrastive Graph Clustering forCommunity Detection and Tracking
Authors: Namyong Park, Ryan A. Rossi, Eunyee Koh, Iftikhar Ahamath Burhanuddin, Sungchul Kim, Fan Du, Nesreen K. Ahmed, Christos Faloutsos
Venue: WWW
Year: 2022

Given entities and their interactions in the web data, which may have occurred at different time, how can we find communities of entities and track their evolution? In this paper, we approach this important task from graph clustering perspective. Recently, state-of-the-art clustering performance in various domains has been achieved by deep clustering methods. Especially, deep graph clustering (DGC) methods have successfully extended deep clustering to graph-structured data by learning node representations and cluster assignments in a joint optimization framework. Despite some differences in modeling choices (e.g., encoder architectures), existing DGC methods are mainly based on autoencoders and use the same clustering objective with relatively minor adaptations. Also, while many real-world graphs are dynamic, previous DGC methods considered only static graphs. In this work, we develop CGC, a novel end-to-end framework for graph clustering, which fundamentally differs from existing methods. CGC learns node embeddings and cluster assignments in a contrastive graph learning framework, where positive and negative samples are carefully selected in a multi-level scheme such that they reflect hierarchical community structures and network homophily. Also, we extend CGC for time-evolving data, where temporal graph clustering is performed in an incremental learning fashion, with the ability to detect change points. Extensive evaluation on real-world graphs demonstrates that the proposed CGC consistently outperforms existing methods.
Using Web Data to Reveal 22-Year History of Sneaker Designs
Authors: Sungkyu Park, Hyeonho Song, Sungwon Han, Berhane Weldegebriel, Lev Manovich, Emanuele Arielli, Meeyoung Cha
Venue: WWW
Year: 2022

Web data and computational models can play important roles in analyzing cultural trends. The current study presents an analysis of 23,492 sneaker images and metadata collected from a global reselling shop, StockX.com. Based on data encompassing 22 years from 1999 to 2020, we propose a sneaker design index that helps track changes in the design characteristics of sneakers using a contrastive learning method. Our data suggest that sneaker designs have been employing brighter colors and lower hue and saturation values over time. We also observe how popular brands have continued to build their unique identities in shape-related design space. The embedding analysis also predicts which sneakers will likely see a high premium in the reselling market, suggesting viable algorithm-driven investment and design strategies. The current work is one of the first publicly available studies to analyze product design evolution over a long historical period and has implications for the novel use of Web data to understand cultural patterns that are otherwise difficult to assess.
Cross-Language Learning for Product Matching
Authors: Ralph Peeters, Christian Bizer
Venue: WWW
Year: 2022

Transformer-based entity matching methods have significantly moved the state of the art for less-structured matching tasks such as matching product offers in e-commerce. In order to excel at these tasks, Transformer-based matching methods require a decent amount of training pairs. Providing enough training data can be challenging, especially if a matcher for non-English product descriptions should be learned. This poster explores along the use case of matching product offers from different e-shops to which extent it is possible to improve the performance of Transformer-based matchers by complementing a small set of training pairs in the target language, German in our case, with a larger set of English-language training pairs. Our experiments using different Transformers show that extending the German set with English pairs improves the matching performance in all cases. The impact of adding the English pairs is especially high in low-resource settings in which only a rather small number of non-English pairs is available. As it is often possible to automatically gather English training pairs from the Web by exploiting schema.org annotations, our results are relevant for many product matching scenarios targeting low-resource languages.
Supervised Contrastive Learning for Product Matching
Authors: Ralph Peeters, Christian Bizer
Venue: WWW
Year: 2022

Contrastive learning has moved the state of the art for many tasks in computer vision and information retrieval in recent years. This poster is the first work that applies supervised contrastive learning to the task of product matching in e-commerce using product offers from different e-shops. More specifically, we employ a supervised contrastive learning technique to pre-train a Transformer encoder which is afterward fine-tuned for the matching task using pair-wise training data. We further propose a source-aware sampling strategy that enables contrastive learning to be applied for use cases in which the training data does not contain product identifiers. We show that applying supervised contrastive pre-training in combination with source-aware sampling significantly improves the state-of-the-art performance on several widely used benchmarks: For Abt-Buy, we reach an F1-score of 94.29 (+3.24 compared to the previous state-of-the-art), for Amazon-Google 79.28 (+ 3.7). For WDC Computers datasets, we reach improvements between +0.8 and +8.84 in F1-score depending on the training set size. Further experiments with data augmentation and self-supervised contrastive pre-training show that the former can be helpful for smaller training sets while the latter leads to a significant decline in performance due to inherent label noise. We thus conclude that contrastive pre-training has a high potential for product matching use cases in which explicit supervision is available.
MagNet: Cooperative Edge Caching by Automatic Content Congregating
Authors: Junkun Peng, Qing Li, Xiaoteng Ma, Yong Jiang, Yutao Dong, Chuang Hu, Meng Chen
Venue: WWW
Year: 2022

Nowadays, the surge of Internet contents and the need for high Quality of Experience (QoE) put the backbone network under unprecedented pressure. The emerging edge caching solutions help ease the pressure by caching contents closer to users. However, these solutions suffer from two challenges: 1) a low hit ratio due to edges’ high density and small coverages. 2) unbalanced edges’ workloads caused by dynamic requests and heterogeneous edge capacities. In this paper, we formulate a typical cooperative edge caching problem and propose the MagNet, a decentralized and cooperative edge caching system to address these two challenges. The proposed MagNet system consists of two innovative mechanisms: 1) the Automatic Content Congregating (ACC), which utilizes a neural embedding algorithm to capture underlying patterns of historical traces to cluster contents into some types. The ACC then can guide requests to their optimal edges according to their types so that contents congregate automatically in different edges by type. This process forms a virtuous cycle between edges and requests, driving a high hit ratio. 2) the Mutual Assistance Group (MAG), which lets idle edges share overloaded edges’ workloads by forming temporary groups promptly. To evaluate the performance of MagNet, we conduct experiments to compare it with classical, Machine Learning (ML)-based and cooperative caching solutions using the real-world trace. The results show that the MagNet can improve the hit ratio from 40% and 60% to 75% for non-cooperative and cooperative solutions, respectively, and significantly improve the balance of edges’ workloads.
Enhancing Multilingual Accessibility of Question Answering over Knowledge Graphs
Authors: Aleksandr Perevalov
Venue: WWW
Year: 2022

There are more than 7000 languages spoken in the world today. Yet, English dominates in many research communities, in particular in the field of Knowledge Graph Question Answering (KGQA). The goal of a KGQA system is to provide natural-language access to a knowledge graph. While many research works aim to achieve the best possible QA quality over English benchmarks, only a small portion of them focuses on providing these systems in a way that different user groups (e.g., speakers of different languages) may use them with the same efficiency (i.e., accessibility). To address this research gap, we investigate the multilingual aspect of the accessibility, which enables speakers of different languages (including low-resource and endangered languages) to interact with KGQA systems with the same efficiency.
Can Machine Translation be a Reasonable Alternative for Multilingual Question Answering Systems over Knowledge Graphs?
Authors: Aleksandr Perevalov, Andreas Both, Dennis Diefenbach, Axel-Cyrille Ngonga Ngomo
Venue: WWW
Year: 2022

Providing access to information is the main and most important purpose of the Web. However, despite available easy-to-use tools (e.g., search engines, chatbots, question answering) the accessibility is typically limited by the capability of using the English language. This excludes a huge amount of people. In this work, we discuss Knowledge Graph Question Answering (KGQA) systems that aim at providing natural language access to data stored in Knowledge Graphs (KG). While several KGQA systems have been proposed, only very few have dealt with a language other than English. In this work, we follow our research agenda of enabling speakers of any language to access the knowledge stored in KGs. Because of the lack of native support for many languages, we use machine translation (MT) tools to evaluate KGQA systems regarding questions in languages that are unsupported by a KGQA system. In total, our evaluation is based on 8 different languages (including some that never were evaluated before). For the intensive evaluation, we extend the QALD-9 dataset for KGQA with Wikidata queries and high-quality translations. The extension was done in a crowdsourcing manner by native speakers of the different languages. By using multiple KGQA systems for the evaluation, we were enabled to investigate and answer the main research question: “Can MT be an alternative for multilingual KGQA systems?”. The evaluation results demonstrated that the monolingual KGQA systems can be effectively ported to the new languages with MT tools.
COnSeNT 2022: 2nd International Workshop on Consent Management in Online Services, Networks and Things
Authors: Paulina Jo Pesch, Harshvardhan J. Pandit, Vitor Jesus, Cristiana Teixeira Santos
Venue: WWW
Year: 2022

ACM Reference Format: Paulina J. Pesch, Harshvardhan J. Pandit, Vitor Jesus, and Cristiana Santos. 2022. COnSeNT 2022: 2nd International Workshop on Consent Management in Online Services, Networks and Things. In Companion Proceedings of the Web Conference 2022 (WWW ’22 Companion), April 25–29, 2022, Virtual Event, Lyon, France. ACM, New York, NY, USA, 5 pages. https://doi.org/10. 1145/3487553.3524866