OpenRaiser/Intern-Atlas huggingface dataset:
Multi-touch Attribution for Complex B2B Customer Journeys using Temporal Convolutional Networks
Authors: Aniket Agrawal, Nikhil Sheoran, Sourav Suman, Gaurav Sinha
Venue: WWW
Year: 2022

Customer journeys in Business-to-Business (B2B) transactions contain long and complex sequences of interactions between different stakeholders from the buyer and seller companies. On the seller side, there is significant interest in the multi-touch attribution (MTA) problem, which aims to identify the most influential stage transitions (in the B2B customer funnel), channels, and touchpoints. We design a novel deep learning-based framework, which solves these attribution problems by modeling the conversion of journeys as functions of stage transitions that occur in them. Each stage transition is modeled as a Temporal Convolutional Network (TCN) on the touchpoints that precede it. Further, a global conversion model Stage-TCN is built by combining these individual stage transition models in a non-linear fashion. We apply Layer-wise Relevance Propagation (LRP) based techniques to compute the relevance of all nodes and inputs in our network and use these to compute the required attribution scores. We run extensive experiments on two real-world B2B datasets and demonstrate superior accuracy of the conversion model compared to prior works. We validate the attribution scores using perturbation-based techniques that measure the change in model output when parts of the input having high attribution scores are deleted.
LocWeb2022: 12th International Workshop on Location and the Web
Authors: Dirk Ahlers, Erik Wilde
Venue: WWW
Year: 2022

The LocWeb2022 workshop at The Web Conference 2022 will run for the 12th time with evolving topics around location-aware information access, Web architecture, spatial social computing, and social good. It is designed as a meeting place for researchers around the location topic at The Web Conference and takes an interdisciplinary perspective.
JGCL: Joint Self-Supervised and Supervised Graph Contrastive Learning
Authors: Selahattin Akkas, Ariful Azad
Venue: WWW
Year: 2022

Semi-supervised and self-supervised learning on graphs are two popular avenues for graph representation learning. We demonstrate that no single method from semi-supervised and self-supervised learning works uniformly well for all settings in the node classification task. Self-supervised methods generally work well with very limited training data, but their performance could be further improved using the limited label information. We propose a joint self-supervised and supervised graph contrastive learning (JGCL) to capture the mutual benefits of both learning strategies. JGCL utilizes both supervised and self-supervised data augmentation and a joint contrastive loss function. Our experiments demonstrate that JGCL and its variants are one of the best performers across various proportions of labeled data when compared with state-of-the-art self-supervised, unsupervised, and semi-supervised methods on various benchmark graphs.
Second International Workshop on the Efficiency of Modern Data Centers (EMDC) Chairs' Welcome
Authors: Eyhab Al-Masri, Di Wang
Venue: WWW
Year: 2022

It is our great pleasure to welcome you to the Second International Workshop on the Efficiency ofModernData Centers (EMDC). Today, data centers have become instrumental for powering software applications and indispensable for data communication. Nonetheless, there exists major research challenges in the day-to-day operations of data centers such as performance, power efficiency, availability, scalability, security among many others. As the number of Internet of Things (IoT) devices proliferates, data center capabilities will need to transcend basic management operations. That is, traditional management capabilities for CPU, memory, input/output and network operations need to be replaced with more advanced IoT-based management capabilities to include items such as temperature sensors, fan speed sensors, power sensors, moisture sensors among many others. Hence, managing large volumes of collected data at high velocities requires efficient distributed training of deep learning models and inference optimization within data centers. Many of the existing modern data centers continuously collect and aggregate a wide range of telemetry data in order to avoid critical downtimes. For example, as heat load of modern data centers increases, the ability to monitor and manage ambient temperature becomes more and more vital for the availability, reliability, serviceability, safety, manageability and scalability of these mission-critical assets. However, current management capabilities also contribute to the consumption of network bandwidth, computational processing power and data storage. Therefore, there is an apparent need for more rigorous architectures and design methods for enhancing the efficiency of modern data centers, more sophisticated design and simulation tools, efficient distributed deep learning models, reliable equipment and software systems benchmarks, accurate performance evaluation methods, among many others. The EMDC workshop aims to provide researchers and practitioners a venue to discuss the current state of today’s modern data centers. The EMDC workshop’s ambition is to help in shaping a community of interest on the existing research opportunities and
Measuring the Privacy Dimension of Free Content Websites through Automated Privacy Policy Analysis and Annotation
Authors: Abdulrahman Alabduljabbar, David Mohaisen
Venue: WWW
Year: 2022

Websites that provide books, music, movies, and other media free of charge are a central piece of the web ecosystem, although they are vastly unexplored, especially for their security and privacy risks. In this paper, we contribute to the understanding of those websites by focusing on the comparative analysis of their privacy policies, a primary channel where service providers inform users about their data collection and use. To better understand the data usage risks associated with such websites, we study 1,562 websites and their privacy policies in contrast to premium websites. We uncover that premium websites are more transparent in reporting their privacy practices, particularly in categories such as “Data Retention” and “Do Not Track”, with premium websites are 85.00% and ≈ 70% more likely to report their practices in comparison to the free content websites. We found the free content websites’ privacy policies to be more similar to one another and generic in comparison to the premium websites’ privacy policies. Our findings raise several concerns, including that the reported privacy policies may not reflect the data collection practices used by service providers, and various pronounced biases across privacy policy categories. This calls for further investigation of the risks associated with the usage of such free content websites and services through active measurements.
Have you been misinformed?: Computational tools and analysis of our interactions with false and corrective information
Authors: Harith Alani
Venue: WWW
Year: 2022

Misinformation has always been part of humankind’s information ecosystem. The development of tools and methods for automatically detecting the reliability of information has received a great deal of attention in recent years, such as calculating the authenticity of images, calculating the likelihood of claims, and assessing the credibility of sources. Unfortunately, there is little evidence that the presence of these advanced technologies or the constant effort of fact-checkers worldwide can help stop the spread of misinformation. I will try to convince you that you also hold various false beliefs, and argue for the need for technologies and processes to assess the information shared by ourselves or by others, over a longer period of time, in order to improve our knowledge of our information credibility and vulnerability, as well as those of the people we listen to. Also, I will describe the benefits, challenges, and risks of automated information corrective actions, both for the target recipients and their wider audience.
HybEx: A Hybrid Tool for Template Extraction
Authors: Julián Alarte, Josep Silva
Venue: WWW
Year: 2022

HybEx is a site-level web template extractor that combines two algorithms for template and content extraction: (i) TemEx, a site-level template detection technique, and (ii) Page-level ConEx, a content extraction technique. The key idea is to add a preprocess to TemEx that removes the main content inferred by Page-level ConEx. It is a fact that adding this new phase to the TemEx algorithm involves an increase of its runtime, however, this increase is very small compared to the TemEx runtime because Page-level ConEx is a page-level technique. On the other hand, HybEx improves the precision, recall, and F1 of TemEx. This paper describes the new template extractor and its internal architecture. Furthermore, the paper also presents the results of its empirical evaluation.
WebAndTheCity'22: 8th International Workshop on The Web and Smart Cities
Authors: Leonidas G. Anthopoulos, Marijn Janssen, Vishanth Weerakkody
Venue: WWW
Year: 2022

This is the 8th edition of the workshop series labeled “AW4City – Web Applications and Smart Cities”, which started back in Florence in 2015 and kept on taking place every year in conjunction with the WWW conference series. Last year the workshop was held virtually in Ljubljana, Slovenia. The workshop series aims to investigate the Web and Web applications’ role in establishing smart city (SC) promises. The workshop series aim to investigate the role of the Web and of Web applications in SC growth. This year, the workshop focuses on the role of the web in smart environment. In the era of cities and under the UN 2030 Agenda and the European Green deal for sustainable growth, cities appear to play crucial role in securing humanity against environmental threats and generate sustainable and circular cities. In this regard, cities attempt to improve their forms (e.g., more compact, and eco-friendlier) and performance to become friendlier and able to host their increasing populations. Additionally, new types of business appear (e.g., that utilize IoT and data, manage e-waste and recycle), while the co-existence of autonomous things and people generate another challenge that cities have been started phasing. This workshop aims to demonstrate how web applications, Apps and Web Intelligence can serve smart environment in general.
Exploiting Geodata to Improve Image Recognition with Deep Learning
Authors: Christian Arbinger, Martin Bullin, Andreas Henrich
Venue: WWW
Year: 2022

Due to the widespread availability of smartphones and digital cameras with GPS functionality, the number of photos associated with geographic coordinates or geoinformation on the internet is continuously increasing. Besides the obvious benefits of geotagged images for the users, geodata can enable a better understanding of the image content and thus facilitate their classification. This work shows the added value of integrating auxiliary geodata during a multi class single label image classification task. Various ways of encoding and extracting auxiliary features from raw coordinates are compared, followed by an investigation of approaches to integrate these features into a convolutional neural network (CNN) by fusion models. We show the classification improvements of adding the raw coordinates and derived auxiliary features such as satellite photos and location-related texts (address information and tags). The results show that the best performance is achieved by a fusion model, which is incorporating textual features based on address information. It is improving the performance the most while reducing the training time: The accuracy of the considered 25 concepts was increased to 85%, compared to 71% in the baseline, while the training time was reduced by 21%. Adding the satellite photos into the neural network shows significant performance improvements as well, but increases the training time. In contrast, numerical features derived directly from raw coordinates do not yield a convincing improvement in classification performance.
Understanding Financial Information Seeking Behavior from User Interactions with Company Filings
Authors: Mozhdeh Ariannezhad, Mohamed Yahya, Edgar Meij, Sebastian Schelter, Maarten de Rijke
Venue: WWW
Year: 2022

Publicly-traded companies are required to regularly file financial statements and disclosures. Analysts, investors, and regulators leverage these filings to support decision making, with high financial and legal stakes. Despite their ubiquity in finance, little is known about the information seeking behavior of users accessing such filings. In this work, we present the first study of this behavior. We analyze 14 years of logs of users accessing company filings of more than 600K distinct companies on the U.S. Securities and Exchange Commission’s (SEC) Electronic Data Gathering, Analysis, and Retrieval (EDGAR) system, the primary resource for accessing company filings. We provide an analysis of the information-seeking behavior for this high-impact domain. We find that little behavioral history is available for the majority of users, while frequent users have rich histories. Most sessions focus on filings belonging to a small number of companies, and individual users are interested in a limited number of companies. Out of all sessions, 66% contain filings from one or two companies and 50% of frequent users are interested in six companies or less. Understanding user interactions with EDGAR can suggest ways to enhance the user journey in browsing filings, e.g., via filing recommendation. Our work provides a stepping stone for the academic community to tackle retrieval and recommendation tasks for the finance domain.
The Case of SPARQL UNION, FILTER and DISTINCT
Authors: Medha Atre
Venue: WWW
Year: 2022

SPARQL’s Basic Graph Pattern (BGP) queries are well-researched for query optimisation and RDF indexing techniques. They resemble SQL inner joins and benefit from reorderability of triple patterns in their performance optimisation. But other components of SPARQL such as OPTIONAL, UNION, FILTER, DISTINCT pose more challenges, as they are part of the SPARQL recursive grammar and impose restrictions on the reorderability of triple patterns. These components are important because they help in querying the semi-structured data as opposed to the strictly structured relational data with a stringent schema. In this paper, we use previously published optimisation techniques for BGP-OPTIONAL queries as primitives, and show how they can be used for SPARQL queries with any intermix of UNION, FILTER, and DISTINCT clauses. We mainly focus on the structural aspects of these queries, identify UNION, FILTER, and DISTINCT queries that can use these BGP-OPTIONAL optimisation techniques, and extend some of the previously published theoretical results.
Towards Preserving Server-Side Privacy of On-Device Models
Authors: Akanksha Atrey, Ritwik Sinha, Somdeb Sarkhel, Saayan Mitra, David Arbour, Akash Maharaj, Prashant J. Shenoy
Venue: WWW
Year: 2022

Machine learning-based predictions are popular in many applications including healthcare, recommender systems and finance. More recently, the development of low-end edge hardware (e.g., Apple’s Neural Engine and Intel’s Movidius VPU) has provided a path for the proliferation of machine learning on the edge with on-device modeling. Modeling on the device reduces latency and helps maintain the user’s privacy. However, on-device modeling can leak private server-side information. In this work, we investigate on-device machine learning models that are used to provide a service and propose novel privacy attacks that can leak sensitive proprietary information of the service provider. We demonstrate that different adversaries can easily exploit such models to maximize their profit and accomplish content theft. Motivated by the need to preserve both client and server privacy, we present preliminary ideas on thwarting such attacks.
Beyond Customer Lifetime Valuation: Measuring the Value of Acquisition and Retention for Subscription Services
Authors: Hamidreza Badri, Allen Tran
Venue: WWW
Year: 2022

Understanding the value of acquiring or retaining subscribers is crucial for subscription-based businesses. While customer lifetime value (LTV) is commonly used to do so, we demonstrate that LTV likely over-states the true value of acquisition or retention. We establish a methodology to estimate the monetary value of acquired or retained subscribers based on estimating both on and off-service LTV. To overcome the lack of data on off-service households, we use an approach based on Markov chains that recovers off-service LTV from minimal data on non-subscriber transitions. Furthermore, we demonstrate how the methodology can be used to (i) forecast aggregate subscriber numbers that respect both aggregate market constraints and account-level dynamics, (ii) estimate the impact of price changes on revenue and subscription growth and (iii) provide optimal policies, such as price discounting, that maximize expected lifetime revenue.
MAISoN'22: 8th International Workshop on Mining Actionable Insights from Social Networks Special Edition on Mental Health and Social Media
Authors: Ebrahim Bagheri, Diana Inkpen, Christopher C. Yang, Fattane Zarrinkalam
Venue: WWW
Year: 2022

The eighth edition of the workshop on Mining Actionable Insights from Social Networks (MAISoN 2022) took place virtually on April 26th, 2022, co-located with the ACM Web Conference 2022 (WWW 2022). This year, we organized a special edition with focus on mental health and social media. The aim of this edition was to bring together researchers from different disciplines to discuss research that goes beyond descriptive analysis of social media data and instead investigate different techniques that use social media data for building diagnostic, predictive and prescriptive analysis models for mental health applications. This topic attracted a lot of interest from the community especially because of all the considerations surrounding the impact of social media during the COVID-19 pandemic which has impacted on people’s mental health issues.
Ready Player One! Eliciting Diverse Knowledge Using A Configurable Game
Authors: Agathe Balayn, Gaole He, Andrea Hu, Jie Yang, Ujwal Gadiraju
Venue: WWW
Year: 2022

Access to commonsense knowledge is receiving renewed interest for developing neuro-symbolic AI systems, or debugging deep learning models. Little is currently understood about the types of knowledge that can be gathered using existing knowledge elicitation methods. Moreover, these methods fall short of meeting the evolving requirements of several downstream AI tasks. To this end, collecting broad and tacit knowledge, in addition to negative or discriminative knowledge can be highly useful. Addressing this research gap, we developed a novel game with a purpose, ‘FindItOut’, to elicit different types of knowledge from human players through easily configurable game mechanics. We recruited 125 players from a crowdsourcing platform, who played 2430 rounds, resulting in the creation of more than 150k tuples of knowledge. Through an extensive evaluation of these tuples, we show that FindItOut can successfully result in the creation of plural knowledge with a good player experience. We evaluate the efficiency of the game (over 10 × higher than a reference baseline) and the usefulness of the resulting knowledge, through the lens of two downstream tasks — commonsense question answering and the identification of discriminative attributes. Finally, we present a rigorous qualitative analysis of the tuples’ characteristics, that informs the future use of FindItOut across various researcher and practitioner communities.
Geospatial Entity Resolution
Authors: Pasquale Balsebre, Dezhong Yao, Gao Cong, Zhen Hai
Venue: WWW
Year: 2022

A geospatial database is today at the core of an ever increasing number of services. Building and maintaining it remains challenging due to the need to merge information from multiple providers. Entity Resolution (ER) consists of finding entity mentions from different sources that refer to the same real world entity. In geospatial ER, entities are often represented using different schemes and are subject to incomplete information and inaccurate location, making ER and deduplication daunting tasks. While tremendous advances have been made in traditional entity resolution and natural language processing, geospatial data integration approaches still heavily rely on static similarity measures and human-designed rules. In order to achieve automatic linking of geospatial data, a unified representation of entities with heterogeneous attributes and their geographical context, is needed. To this end, we propose Geo-ER1, a joint framework that combines Transformer-based language models, that have been successfully applied in ER, with a novel learning-based architecture to represent the geospatial character of the entity. Different from existing solutions, Geo-ER does not rely on pre-defined rules and is able to capture information from surrounding entities in order to make context-based, accurate predictions. Extensive experiments on eight real world datasets demonstrate the effectiveness of our solution over state-of-the-art methods. Moreover, Geo-ER proves to be robust in settings where there is no available training data for a specific city.
Sensor Network Design for Uniquely Identifying Sources of Contamination in Water Distribution Networks
Authors: Kaustav Basu, Arunabha Sen
Venue: WWW
Year: 2022

Sensors are being extensively adopted for use in smart cities in order to monitor various parameters, so that any anomalous behaviours manifesting in the deployment area, can be easily detected. Sensors in a deployment area have two functions, sensing/coverage and communication, with this paper focusing on the former. Over the years, several coverage models have been proposed which utilizes the Set Cover based problem formulation. This formulation unfortunately has a drawback, in the sense that it lacks unique identification capability for the location where anomalous behavior is sensed. This limitation can be overcome through utilization of Identifying Code. The optimal solution of the Identifying Code problem provides the minimum number of sensors that will be needed to uniquely identify the location where anomalous behavior is sensed. In this paper, we introduce a novel budget constrained version of the problem, whose goal is to find the largest number of locations that can be uniquely identified with the sensors that can be deployed within the specified budget. We provide an Integer Linear Programming formulation and a Maximum Set-Group Cover (MSGC) formulation for the problem and prove that the MSGC problem cannot have a polynomial time approximation algorithm with a 1/k factor performance guarantee unless P = NP.