OpenRaiser/Intern-Atlas huggingface dataset:
MEMER - Multimodal Encoder for Multi-signal Early-stage Recommendations
Authors: Mohit Agarwal, Srijan Saket, Rishabh Mehrotra
Venue: WWW
Year: 2023

Millions of content gets created daily on platforms like YouTube, Facebook, TikTok etc. Most of such large scale recommender systems are data demanding, thus taking substantial time for content embedding to mature. This problem is aggravated when there is no behavioral data available for new content. Poor quality recommendation for these items lead to user dissatisfaction and short content shelf-life. In this paper we propose a solution MEMER (Multimodal Encoder for Multi-signal Early-stage Recommendations), that utilises the multimodal semantic information of content and uses it to generate better quality embeddings for early-stage items. We demonstrate the flexibility of the framework by extending it to various explicit and implicit user actions. Using these learnt embeddings, we conduct offline and online experiments to verify its effectiveness. The predicted embeddings show significant gains in online early-stage experiments for both videos and images (videos: 44% relative gain in click through rate, 46% relative gain in explicit engagements, 9% relative gain in successful video play, 20% relative reduction in skips, images: 56% relative gain in explicit engagements). This also compares well against the performance of mature embeddings (83.3% RelaImpr (RI) [18] in Successful Video Play, 97.8% RelaImpr in Clicks).
Is the Impression Log Beneficial to Effective Model Training in News Recommender Systems? No, It's NOT
Authors: Jeewon Ahn, Hong-Kyun Bae, Sang-Wook Kim
Venue: WWW
Year: 2023

The MIND dataset, one of the most-popular real-world news datasets, has been used in many news recommendation researches. They all employ the impression log as training data to train their models (i.e., impression-based training). In this paper, we claim that the impression log has the preference bias issue and thus should not be used for model training in news recommendation. We validate our claim via extensive experiments; we also demonstrate the dramatic improvement of recommendation accuracy in five existing state-of-the-art models up to 82.1% by simply ignoring the impression log. We believe this surprising result provides a new insight towards better model training for both researchers and practitioners working in a news recommendation area.
A New Annotation Method and Dataset for Layout Analysis of Long Documents
Authors: Aman Ahuja, Kevin Dinh, Brian Dinh, William A. Ingram, Edward A. Fox
Venue: WWW
Year: 2023

Parsing long documents, such as books, theses, and dissertations, is an important component of information extraction from scholarly documents. Layout analysis methods based on object detection have been developed in recent years to help with PDF document parsing. However, several challenges hinder the adoption of such methods for scholarly documents such as theses and dissertations. These include (a) the manual effort and resources required to annotate training datasets, (b) the scanned nature of many documents and the inherent noise present resulting from the capture process, and (c) the imbalanced distribution of various types of elements in the documents. In this paper, we address some of the challenges related to object detection based layout analysis for scholarly long documents. First, we propose an AI-aided annotation method to help develop training datasets for object detection based layout analysis. This leverages the knowledge of existing trained models to help human annotators, thus reducing the time required for annotation. It also addresses the class imbalance problem, guiding annotators to focus on labeling instances of rare classes. We also introduce ETD-ODv2, a novel dataset for object detection on electronic theses and dissertations (ETDs). In addition to the page images included in ETD-OD [1], our dataset consists of more than 16K manually annotated page images originating from 100 scanned ETDs, along with annotations for 20K page images primarily consisting of rare classes that were labeled using the proposed framework. The new dataset thus covers a diversity of document types, viz., scanned and born-digital, and is better balanced in terms of training samples from different object categories.
Mapping Flood Exposure, Damage, and Population Needs Using Remote and Social Sensing: A Case Study of 2022 Pakistan Floods
Authors: Zainab Akhtar, Umair Qazi, Rizwan Sadiq, Aya El-Sakka, Muhammad Sajjad, Ferda Ofli, Muhammad Imran
Venue: WWW
Year: 2023

The devastating 2022 floods in Pakistan resulted in a catastrophe impacting millions of people and destroying thousands of homes. While disaster management efforts were taken, crisis responders struggled to understand the country-wide flood extent, population exposure, urgent needs of affected people, and various types of damage. To tackle this challenge, we leverage remote and social sensing with geospatial data using state-of-the-art machine learning techniques for text and image processing. Our satellite-based analysis over a one-month period (25 Aug–25 Sep) revealed that 11.48% of Pakistan was inundated. When combined with geospatial data, this meant 18.9 million people were at risk across 160 districts in Pakistan, with adults constituting 50% of the exposed population. Our social sensing data analysis surfaced 106.7k reports pertaining to deaths, injuries, and concerns of the affected people. To understand the urgent needs of the affected population, we analyzed tweet texts and found that South Karachi, Chitral and North Waziristan required the most basic necessities like food and shelter. Further analysis of tweet images revealed that Lasbela, Rajanpur, and Jhal Magsi had the highest damage reports normalized by their population. These damage reports were found to correlate strongly with affected people reports and need reports, achieving an R-Square of 0.96 and 0.94, respectively. Our extensive study shows that combining remote sensing, social sensing, and geospatial data can provide accurate and timely information during a disaster event, which is crucial in prioritizing areas for immediate and gradual response.
Depicting Vocabulary Summaries with Devos
Authors: Ahmad Alobaid, Jhon Toledo, Óscar Corcho, María Poveda-Villalón
Venue: WWW
Year: 2023

Communicating ontologies to potential users is still a difficult and time-consuming task. Even for small ones, users need to invest time to determine whether to reuse them. Providing diagrams together with the ontologies facilitates the task of understanding the model from a user perspective. While some tools are available for depicting ontologies, and the code could also be inspected using ontology editors’ graphical interfaces, in many cases, the diagrams are too big or complex. The main objective of this demo is to present Devos, a system to generate ontology diagrams based on different strategies for summarizing the ontology.
The Age of Snippet Programming: Toward Understanding Developer Communities in Stack Overflow and Reddit
Authors: Alessia Antelmi, Gennaro Cordasco, Daniele De Vinco, Carmine Spagnuolo
Venue: WWW
Year: 2023

Today, coding skills are among the most required competencies worldwide, often also for non-computer scientists. Because of this trend, community contribution-based, question-and-answer (Q&A) platforms became prominent for finding the proper solution to all programming issues. Stack Overflow has been the most popular platform for technical-related questions for years. Still, recently, some programming-related subreddits of Reddit have become a standing stone for questions and discussions. This work investigates the developers’ behavior and community formation around the twenty most popular programming languages. We examined two consecutive years of programming-related questions from Stack Overflow and Reddit, performing a longitudinal study on users’ posting activity and their high-order interaction patterns abstracted via hypergraphs. Our analysis highlighted crucial differences in how these Q&A platforms are utilized by their users. In line with previous literature, it emphasized the constant decline of Stack Overflow in favor of more community-friendly platforms, such as Reddit, which has been growing rapidly lately.
WebAndTheCity'23: 9th International Workshop on The Web and Smart Cities
Authors: Leonidas G. Anthopoulos, Marijn Janssen, Vishanth Weerakkody
Venue: WWW
Year: 2023

This is the 9th 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 Lyon, France. 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 social coherence. cities appear to play a crucial role in securing humanity against social threats and generating sustainable and circular cities. In this regard, cities attempt to secure social sustainability and coherence (e.g., deal with affordable energy, poverty, hunger, equal opportunities in education, jobs, and health, etc.) and enhance their performance to become friendlier and able to host their increasing populations. Additionally, new types of business appear (e.g., for smart energy), while the co-existence of autonomous things and people generate another challenge that cities have started phasing. This workshop aims to demonstrate how web applications Apps can Web intelligence serve communities.
A ML-based Approach for HTML-based Style Recommendation
Authors: Ryan Aponte, Ryan A. Rossi, Shunan Guo, Jane Hoffswell, Nedim Lipka, Chang Xiao, Gromit Yeuk-Yin Chan, Eunyee Koh, Nesreen K. Ahmed
Venue: WWW
Year: 2023

Given a large corpus of HTML-based emails (or websites, posters, documents) collected from the web, how can we train a model capable of learning from such rich heterogeneous data for HTML-based style recommendation tasks such as recommending useful design styles or suggesting alternative HTML designs? To address this new learning task, we first decompose each HTML document in the corpus into a sequence of smaller HTML fragments where each fragment may consist of a set of HTML entities such as buttons, images, textual content (titles, paragraphs) and stylistic entities such as background-style, font-style, button-style, among others. From these HTML fragments, we then derive a single large heterogeneous hypergraph that captures the higher-order dependencies between HTML fragments and entities in such fragments, both within the same HTML document as well as across the HTML documents in the corpus. We then formulate this new HTML style recommendation task as a hypergraph representation learning problem and propose an approach to solve it. Our approach is able to learn effective low-dimensional representations of the higher-order fragments that consist of sets of heterogeneous entities as well as low-dimensional representations of the individual entities themselves. We demonstrate the effectiveness of the approach across several design style recommendation tasks. To the best of our knowledge, this work is the first to develop an ML-based model for the task of HTML-based email style recommendation.
Decolonizing Creative Labor in the age of AI
Authors: Payal Arora
Venue: WWW
Year: 2023

Creative AI has got us asking existential questions of what makes us human. To crack the code, you need to crack the culture that makes us who we are. Who and what is creative remains largely disconnected from diverse and global cultural norms, rendering existing technology suboptimal and even unusable to the world’s majority. Creativity has long been dictated by the aesthetic taste, values, needs, concerns, and aspirations of the West. Today, India and China alone account for the majority of the world’s users. The Global South are fast shaping data systems in ways that remain underexamined and siloed as “Rest of World” among industry and government folks. With the rise of the creator economy across sectors, questions abound on creative rights, provenance, fairness, labor, and representation. This talk discusses concerns around digital labor, data materiality, media literacies, creative value, and online expression. In doing so, it sets a pathway towards designing inclusive and intersectional systems that transcend borders.
Detecting Cross-Lingual Information Gaps in Wikipedia
Authors: Vahid Ashrafimoghari
Venue: WWW
Year: 2023

An information gap exists across Wikipedia’s language editions, with a considerable proportion of articles available in only a few languages. As an illustration, it has been observed that 10 languages possess half of the available Wikipedia articles, despite the existence of 330 Wikipedia language editions. To address this issue, this study presents an approach to identify the information gap between the different language editions of Wikipedia. The proposed approach employs Latent Dirichlet Allocation (LDA) to analyze linked entities in a cross-lingual knowledge graph in order to determine topic distributions for Wikipedia articles in 28 languages. The distance between paired articles across language editions is then calculated. The potential applications of the proposed algorithm to detecting sources of information disparity in Wikipedia are discussed, and directions for future research are put forward.
Smart Cities as Hubs: a use case from Biotechnology
Authors: Asteria Tsapadikou, Leonidas G. Anthopoulos
Venue: WWW
Year: 2023

Smart city platforms operate as central points of access for locally collected data. The smart city hub (SCHub) introduces a new concept that aims to homogenize data, service, human and material flows in cities. A proof of concept is based on non-typical data flows, like the ones that are collected by biotechnological activities, like Diet-related non-communicable diseases (NCDs). NCDs are responsible for 1 in 5 deaths globally. Most of the diet related NCDs are related to the gut microbiome, the microbial community that resides in our gastrointestinal tract. The imbalance or loss of microbiome diversity is one of the main factors leading to NCDs by affecting various functions, including energy metabolism, intestinal permeability, and brain function. Gut dysbiosis is reflected in altered concentrations of Short Chain Fatty Acids (SCFAs), produced by the gut microbiota. A microcapsule system can play the role of a sensor that collects data from the local community and transmits it to the SCHub in order for the doctors to receive the appropriate patients information and define the appropriate treatment method; for the city to process anonymized information and measure community's health in diet terms. A prototype with a biosensor that correlates the amount of gut SCFAs with gut microbiome functional capacities is presented in this paper, together with the use-case scenario that engages the SCHub.
ProSA: A provenance system for reproducing query results
Authors: Tanja Auge
Venue: WWW
Year: 2023

Good scientific work requires comprehensible, transparent and reproducible research. One way to ensure this is to include all data relevant to a study or evaluation when publishing an article. This data should be at least aggregated or anonymized, at best compact and complete, but always resilient. In this paper we present ProSA, a system for calculating the minimal necessary data set, called sub-database. For this, we combine the Chase — a set of algorithms for transforming databases — with additional provenance information. We display the implementation of provenance guided by the ProSA pipeline and show its use to generate an optimized sub-database. Furhter, we demonstrate how the ProSA GUI looks like and present some applications and extensions.
Provenance for Lattice QCD workflows
Authors: Tanja Auge, Gunnar Bali, Meike Klettke, Bertram Ludäscher, Wolfgang Söldner, Simon Weishäupl, Tilo Wettig
Venue: WWW
Year: 2023

We present a provenance model for the generic workflow of numerical Lattice Quantum Chromodynamics (QCD) calculations, which constitute an important component of particle physics research. These calculations are carried out on the largest supercomputers worldwide with data in the multi-PetaByte range being generated and analyzed. In the Lattice QCD community, a custom metadata standard (QCDml) that includes certain provenance information already exists for one part of the workflow, the so-called generation of configurations. In this paper, we follow the W3C PROV standard and formulate a provenance model that includes both the generation part and the so-called measurement part of the Lattice QCD workflow. We demonstrate the applicability of this model and show how the model can be used to answer some provenance-related research questions. However, many important provenance questions in the Lattice QCD community require extensions of this provenance model. To this end, we propose a multi-layered provenance approach that combines prospective and retrospective elements.
Computing and Visualizing Agro-Meteorological Parameters based on an Observational Weather Knowledge Graph
Authors: Nadia Yacoubi Ayadi, Catherine Faron, Franck Michel, Fabien Gandon, Olivier Corby
Venue: WWW
Year: 2023

Linked-data principles are more and more adopted to integrate and publish semantically described open data using W3C standards resulting in a large amount of available resources [7]. In particular, meteorological sensor data have been uplifted into public RDF graphs, such as WeKG-MF which offers access to a large set of meteorological variables described through spatial and temporal dimensions. Nevertheless, these resources include huge numbers of raw observations that are tedious to be explored and reused by lay users. In this paper, we leverage WeKG-MF to compute important agro-meteorological parameters and views with SPARQL queries. As a result, we deployed a LOD platform as a web application to allow users to navigate, consume and produce linked datasets of agro-meterological parameters calculated on-the-fly.
Those who are left behind: A chronicle of internet access in Cuba
Authors: Brenda Reyes Ayala
Venue: WWW
Year: 2023

This paper presents a personal chronicle of internet access in Cuba from the perspective of a visitor to the island. It is told across three time periods: 1997, 2010, and 2021. The story describes how the island first connected to the internet in the 90s, how internet access evolved throughout the 2000s, and ends in the role the internet played in the government protests on July 11, 2021. The article analyzes how internet access in Cuba has changed over the decades and its effects on civil society. It discusses issues such as Cuba’s technological infrastructure, internet censorship, and free expression.
Gone, Gone, but Not Really, and Gone, But Not forgotten: A Typology of Website Recoverability
Authors: Brenda Reyes Ayala
Venue: WWW
Year: 2023

This paper presents a qualitative analysis of the recoverability of various webpages on the live web, using their archived counterparts as a baseline. We used a heterogeneous dataset consisting of four web archive collections, each with varying degrees of content drift. We were able to recover a small number of webpages previously thought to have been lost and analyzed their content and evolution. Our analysis yielded three types of lost webpages: 1) those that are not recoverable (with three subtypes), 2) those that are fully recoverable, and 3) those that are partially recoverable. The analysis presented here attempts to establish clear definitions and boundaries between the different degrees of webpage recoverabilty. By using a few simple methods, web archivists could discover the new locations of web content that was previously deemed lost, and include them in future crawling efforts, and lead to more complete web archives with less content drift.
Learning to Bid in Contextual First Price Auctions✱
Authors: Ashwinkumar Badanidiyuru, Zhe Feng, Guru Guruganesh
Venue: WWW
Year: 2023

In this work, we investigate the problem of how to bid in repeated contextual first price auctions for a single learner (the bidder). Concretely, at each time t, the learner receives a context and decides the bid based on historical information and xt. We assume that the maximum bid of all the others follows a linear model, i.e., mt = ⟨α0, xt⟩ + zt, where is unknown to the learner and zt is randomly sampled from a noise distribution with log-concave density. In this work, we consider both binary feedback (the learner can only observe whether she wins or not) and full information feedback (the learner can observe mt) models of the learner. For binary feedback, when the noise distribution is (partially) known, we propose a bidding algorithm that achieves at most regret, where Δ is a constant reserve in first price auctions.1 For the full information feedback with unknown noise distribution, we provide an algorithm that achieves regret at most . Our approach combines an estimator for log-concave density functions and the Maximum Likelihood Estimation (MLE) method to learn the noise distribution and linear weight α0 simultaneously. We complement our results with a lower bound result such that any bidding policy in a broad class must achieve regret at least , even when the learner receives the full information feedback and is known.
Expressive user embedding from churn and recommendation multi-task learning
Authors: Huajun Bai, Davide Liu, Thomas Hirtz, Alexandre Boulenger
Venue: WWW
Year: 2023

In this paper, we present a Multi-Task model for Recommendation and Churn prediction (MT) in the retail banking industry. The model leverages a hard parameter-sharing framework and consists of a shared multi-stack encoder with multi-head self-attention and two fully connected task heads. It is trained to achieve two multi-class classification tasks: predicting product churn and identifying the next-best products (NBP) for users, individually. Our experiments demonstrate the superiority of the multi-task model compared to its single-task versions, reaching top-1 precision at 78.1% and 77.6%, for churn and NBP prediction respectively. Moreover, we find that the model learns a coherent and expressive high-level representation reflecting user intentions related to both tasks. There is a clear separation between users with acquisitions and users with churn. In addition, acquirers are more tightly clustered compared to the churners. The gradual separability of churning and acquiring users, who diverge in intent, is a desirable property. It provides a basis for model explainability, critical to industry adoption, and also enables other downstream applications. These potential additional benefits, beyond reducing customer attrition and increasing product use–two primary concerns of businesses, make such a model even more valuable.