OpenRaiser/Intern-Atlas huggingface dataset:
Predicting Company ESG Ratings from News Articles Using Multivariate Timeseries Analysis
Authors: Tanja Aue, Adam Jatowt, Michael Färber
Venue: WWW
Year: 2025

In recent years, corporate environmental, social, and governance (ESG) engagement has received significant public attention. As mandatory ESG reporting is increasingly adopted and investors place greater emphasis on sustainability in their decisions, the demand for transparent and reliable ESG ratings is growing. However, existing automatic approaches to ESG rating prediction remain limited. Many rely on traditional machine learning methods like random forests or social network analysis, rather than leveraging incoming news article streams and large multivariate time series data, which, for the first time, enables capturing the dynamic relationships between topics, sentiments, and events. In this paper, we propose a novel approach to predicting ESG ratings from news articles by uniquely combining multivariate time series construction with advanced deep learning techniques. We create an extensive dataset of 3.7 million news articles spanning three years and covering 3,000 U.S. companies, providing a robust foundation for training and evaluating our approach. Our approach achieves high accuracy and outperforms existing approaches, underscoring its potential as a scalable, data-driven solution for ESG rating prediction.
Does Weighting Improve Matrix Factorization for Recommender Systems?
Authors: Alex Ayoub, Samuel Robertson, Dawen Liang, Harald Steck, Nathan Kallus
Venue: WWW
Year: 2025

Matrix factorization is a widely used approach for top-N recommendation and collaborative filtering. When implemented on implicit feedback data (such as clicks), a common heuristic is to upweight the observed interactions. This strategy has been shown to improve performance for certain algorithms. In this paper, we conduct a systematic study of various weighting schemes and matrix factorization algorithms. Somewhat surprisingly, we find that training with unweighted data can perform comparably to-and sometimes outperform-training with weighted data, especially for large models. This observation challenges the conventional wisdom. Nevertheless, we identify cases where weighting can be beneficial, particularly for models with lower capacity and specific regularization schemes. We also derive efficient algorithms for exactly minimizing several weighted objectives that were previously considered computationally intractable. Our work provides a comprehensive analysis of the interplay between weighting, regularization, and model capacity in matrix factorization for recommender systems.
Evaluating Column Type Annotation Models and Benchmarks
Authors: Amir Babamahmoudi, Davood Rafiei, Mario A. Nascimento
Venue: WWW
Year: 2025

Column Type Annotation (CTA) is the process of assigning semantic types to the columns of a table. Models and approaches for CTA are typically evaluated on distinct datasets, making benchmarking and comparisons not trivial. In this paper, we identify major issues in commonly-used datasets for CTA and explore their impact on the evaluation process and reported results. Drawing from our analysis, we propose changes towards more robust and unbiased benchmarking practices.
Ranking Items by the Current-Preferences and Profits: A List-wise Learning-to-Rank Approach to Profit Maximization
Authors: Hong-Kyun Bae, Hae-Ri Jang, Won-Yong Shin, Sang-Wook Kim
Venue: WWW
Year: 2025

In e-commerce platforms, profit-aware recommender systems aim to improve the platform's profits while maintaining high overall accuracy by recommending items with high profits as top-ranked items. We explore two issues faced by existing model-based profit-aware approaches (i.e., MBAs) when training recommendation models for profit enhancement. First, existing MBAs tend to inaccurately infer the item ranking without considering the user's current preference for each item through their profit-based weighting scheme. Second, through the point-wise learning-to-rank (LTR), the model is optimized solely for the preference score of each item independently rather than being directly optimized for the overall ranking of items. To tackle these issues, we propose a novel MBA that involves three key steps: (S1) defining the Current Preference incorporated with Profit (i.e., CPP) for items; (S2) classifying items through CPP; and (S3) training the model by list-wise LTR based on CPP. Extensive experimental results using real-world platform datasets demonstrate that our approach improves accuracy by approximately 4% and profits by about 24% compared to the best-competing method.
Iterative Proof-Driven Development LLM Prompt
Authors: Aneesha Bakharia
Venue: WWW
Year: 2025

This paper introduces Iterative Proof-Driven Development, a novel prompt engineering method designed to take advantage of GPT-4 level models that are able to follow detailed instructions. Iterative Proof-Driven Development is a structured prompt that outlines a test-driven process for solving complex mathematics and programming tasks. The prompt provides an LLM with instructions to decompose a problem into sub-problems, generate test cases, verify results, and integrate sub-solutions. While Chain of Thought (CoT) reasoning remains central to individual steps, the structured process ensures consistency and reliability. This method takes advantage of embedded code interpreters within chatbot user interfaces, enabling immediate execution, debugging, and iterative refinement without external tools. We demonstrate this prompting technique by guiding an LLM through the development of a small CSV processing library, illustrating each step and highlighting how the method ensures well-tested, logically consistent, and reliable outputs. Finally, we explore potential use cases, future directions for automation with agent-based systems (e.g., Langgraph), and the broader implications of prompt engineering for larger software development projects.
What's in a Query: Polarity-Aware Distribution-Based Fair Ranking
Authors: Aparna Balagopalan, Kai Wang, Olawale Salaudeen, Asia Biega, Marzyeh Ghassemi
Venue: WWW
Year: 2025

Machine learning-driven rankings, where individuals (or items) are ranked in response to a query, mediate search exposure or attention in a variety of safety-critical settings. Thus, it is important to ensure that such rankings are fair. Under the goal of equal opportunity, attention allocated to an individual on a ranking interface should be proportional to their relevance across search queries. In this work, we examine amortized fair ranking -- where relevance and attention are cumulated over a sequence of user queries to make fair ranking more feasible in practice. Unlike prior methods that operate on expected amortized attention for each individual, we define new divergence-based measures for attention distribution-based fairness in ranking (DistFaiR), characterizing unfairness as the divergence between the distribution of attention and relevance corresponding to an individual over time. This allows us to propose new definitions of unfairness, which are more reliable at test time. Second, we prove that group fairness is upper-bounded by individual fairness under this definition for a useful class of divergence measures, and experimentally show that maximizing individual fairness through an integer linear programming-based optimization is often beneficial to group fairness. Lastly, we find that prior research in amortized fair ranking ignores critical information about queries, potentially leading to a fairwashing risk in practice by making rankings appear more fair than they actually are.
Autobidding With Interdependent Values
Authors: Martino Banchio, Kshipra Bhawalkar, Christopher Liaw, Aranyak Mehta, Andrés Perlroth
Venue: WWW
Year: 2025

In this paper, we initiate the study of autobidding where the signals for each bidder can be noisy and correlated. Our first set of results showcases the failure of traditional auctions such as the second-price auction (SPA) and the first-price auction (FPA). In particular, uniform bidding is not an optimal bidding strategy for SPA and both SPA and FPA can have arbitrarily poor efficiency. To circumvent this, we propose the Contextual Second Price Auction (CSPA), a novel mechanism which mitigates the aforementioned adverse effects by leveraging multiple signals to adjust the allocation of SPA. We show that uniform bidding is an optimal bidding strategy in CSPA and we prove a tight bound on the price for anarchy for CSPA of 2, thus recovering the well-established results in the independent setting. Finally, we show that CSPA always achieves at least half the welfare of SPA; moreover this is also tight.
A Cloud-Based High-Quality Code Intelligent Matching Tool for Industrial Control Programs
Authors: Yingzhe Bao, Danfeng Sun, Junhui Liu, Jin Fan, Huifeng Wu
Venue: WWW
Year: 2025

The programmable logic controller (PLC) is the core of automation control systems. With Industry 4.0, PLCs have evolved from simple controllers to intelligent industrial nodes. However, as technologies such as cloud computing and artificial intelligence integrate into industrial automation, traditional monolithic development software struggles with scalability and efficiency, failing to meet the rapid iteration needs of control programs in complex industrial scenarios. This paper introduces a cloud-based high-quality code intelligent matching tool for industrial control programs, aimed at shortening the development time of control systems and enabling rapid iterative development of programs. The matching tool includes two main modules: the code matching module and the code quality assessment module. These modules enable rapid code reuse based on the source program database.
From Division to Unity: A Large-Scale Study on the Emergence of Computational Social Science, 1990-2021
Authors: Honglin Bao, Jiawei Zhang, Mingxuan Cao, James A. Evans
Venue: WWW
Year: 2025

We present a comprehensive study on the emergence of Computational Social Science (CSS) - an interdisciplinary field leveraging computational methods to address social science questions - and its impact on adjacent social sciences. We trained a robust CSS classifier using papers from CSS-focused venues and applied it to 11 million papers spanning 1990 to 2021. Our analysis yielded three key findings. First, there were two critical inflections in the rise of CSS. The first occurred around 2005 when psychology, politics, and sociology began engaging with CSS. The second emerged in approximately 2014 when economics finally joined the trend. Sociology is currently the most engaged with CSS. Second, using the density of yearly knowledge embeddings constructed by advanced transformer models, we observed that CSS initially lacked a cohesive identity. From the early 2000s to 2014, however, it began to form a distinct cluster, creating boundaries between CSS and other social sciences, particularly in politics and sociology. After 2014, these boundaries faded, and CSS increasingly blended with the social sciences. Third, shared data-driven methods homogenized CSS papers across disciplines, with politics and economics showing the most alignment due to the combined influence of CSS and causal identification. Nevertheless, non-CSS papers in sociology, psychology, and politics became more divergent. Taken together, these findings highlight the dynamics of division and unity as new disciplines emerge within existing knowledge landscapes. A live demo of CSS evolution can be found at https://evolution-css.netlify.app/
LP-DIXIT: Evaluating Explanations for Link Predictions on Knowledge Graphs using Large Language Models
Authors: Roberto Barile, Claudia d&apos, Amato, Nicola Fanizzi
Venue: WWW
Year: 2025

Knowledge Graphs provide a machine-readable representation of knowledge conforming to graph-based data models. Link prediction methods predict missing facts in incomplete knowledge graphs, often using scalable embedding based solutions that, however, lack comprehensibility which is crucial in many domains. Filling this gap, explanation methods identify supporting knowledge. For evaluating them, user studies are the obvious choice as users are the main recipients of explanations. However, finding domain experts is often challenging. In contrast, an automated approach is to measure the influence of explanations on the very same link prediction task, thus disregarding the perspective of users. Additionally, current evaluation methods vary across different explanation approaches. We propose LP-DIXIT, the first protocol to evaluate the utility of explanations of link predictions. LP-DIXIT is user-aware, algorithmic and unique for different explanation methods. It builds on a typical setting of user studies, but adopts Large Language Models (LLMs) to mimic users. Specifically, it measures how explanations improve the user (LLM) ability to perform predictions, which is key to trust. We experimentally proved an overall agreement between LP-DIXIT and user evaluations. Moreover, we adopted LP-DIXIT to conduct a comparative study of state-of-the-art explanation methods. The outcomes suggest that less is more: the most effective explanations are those consisting of a single fact.
Efficient Algorithms for Leveraging LLMs for Generative and Predictive Recommender Systems
Authors: Kayhan Behdin, Yun Dai, Gregory Dexter, Aman Gupta, Rahul Mazumder, Ankan Saha, Qingquan Song, Shao Tang, Sirou Zhu, Pin-Lun Hsu
Venue: WWW
Year: 2025

Large language models (LLMs) have taken the world by storm, revolutionizing the use of AI in products. While scaling laws demonstrate that larger models yield better results, making them work in production is hard, often due to latency demands on inference. In this proposed tutorial, we will share optimizations - both algorithmic and systems-related - that help leverage LLMs (both small and large) for recommendation and generative AI use cases at planet scale for the world's largest professional network - LinkedIn. In the first part of the tutorial, we will discuss state-of-the-art (SOTA) model quantization and pruning techniques. This will be in conjunction with a discussion on GPU kernel-level optimizations including minimizing memory copying, effectively utilizing shared memory, optimizing thread scheduling, and maximizing parallel efficiency. We will discuss our own experience with these inventing and leveraging such techniques, while also discussing the latest advancements from other enterprises and the open source world. Our discussions will cover models ranging in size from 1 billion to 100 billion+ parameters. In the second part of the tutorial, we will discuss the latest advancements in the world of LLM knowledge distillation which can result in training very powerful and performant small language models (SLMs). We will also discuss effective instruction tuning and preference alignment techniques that help with improving accuracy and quality of results for generative use cases. Finally, we will discuss actual production use cases that benefit from the aforementioned techniques at planet scale for LinkedIn.
Unveiling Network Performance in the Wild: An Ad-Driven Analysis of Mobile Download Speeds
Authors: Miguel A. Bermejo-Agueda, Patricia Callejo, Rubén Cuevas, Ángel Cuevas, Ramakrishnan Durairajan, Reza Rejaie, Álvaro Mayol
Venue: WWW
Year: 2025

Accurate measurement of mobile network performance is crucial for optimizing user experience and ensuring regulatory compliance. Traditional methods like crowdsourcing approaches, though effective, depend heavily on user participation and extensive infrastructure. In this paper, we introduce adNPM, a novel technique for measuring download speed by embedded measurement code in ads displayed across web browsers and mobile apps, without requiring user participation. Through controlled lab tests and real-world deployments in 15 countries, we demonstrate that adNPM achieves accuracy comparable to well-established tools like Speedtest by Ookla and Opensignal while significantly reducing data consumption. adNPM leverages ad campaigns to collect extensive data from diverse demographics and geographic regions, providing deep insights into the performance of major Internet Service Providers (ISPs). Furthermore, adNPM can segment download speed analyses by demographic factors and operating systems, making it a versatile and scalable tool for network performance assessment.
Investigating Cross-Domain Capabilities of LLMs in Two-Stage Recommender Systems
Authors: Mohamed Islem Kara Bernou, Aghiles Salah, Alexandru Tatar
Venue: WWW
Year: 2025

Leveraging Large Language Models (LLMs) for recommendation is an emerging area with promising advancements across various use cases. Pursuing this line of research, we explore the complex and often overlooked cross-domain scenario in which user preferences from a source domain (e.g., Books) are used to recommend items in a target domain (e.g., Movies). We postulate that LLMs can significantly enhance cross-domain recommendations due to their pre-training on extensive web-scale corpora, which encompass diverse knowledge across languages and domains. However, two key limitations hinder the application of LLMs in this context: input size constraints and the computationally intensive autoregressive generation phase, making real-world implementation challenging. To tackle these issues, we revisit a recently proposed two-stage approach that employs a retrieval model to select a small list of the most relevant items from the catalog, which is subsequently refined by the LLM for final recommendations. This approach effectively addresses input size constraints and eliminates the need for a costly generation phase. Our experiments with various LLMs demonstrate their potential in the cross-domain recommendation context, while emphasizing the need for further research, as not all LLM variants consistently yield improvements.
Amiability and the Web: Lessons from Red Vienna and the Origin of Computing
Authors: Mark Bernstein
Venue: WWW
Year: 2025

From 1919 to 1934, the socialist city government of Vienna fostered a remarkable increase in art, public architecture, and scholarship despite desperate economic conditions. Throughout this period, the Vienna Circle, a philosophical discussion group, examined with new rigor the question of what can be known. Their work built the theoretical foundations of computing. Much of this work was carried out in Vienna's distinctive cafés. This was not a quaint idiosyncrasy; the cafés were in the business of amiability. Parallels to the early Web and its precursors are not difficult to find, and the collapse of Red Vienna may parallel the current predicament of the Web.
Evaluating Personality Traits in Large Language Models: Insights from Psychological Questionnaires
Authors: Pranav Bhandari, Usman Naseem, Amitava Datta, Nicolas Fay, Mehwish Nasim
Venue: WWW
Year: 2025

Psychological assessment tools have long helped humans understand behavioural patterns. While Large Language Models (LLMs) can generate content comparable to that of humans, we explore whether they exhibit personality traits. To this end, this work applies psychological tools to LLMs in diverse scenarios to generate personality profiles. Using established trait-based questionnaires such as the Big Five Inventory and by addressing the possibility of training data contamination, we examine the dimensional variability and dominance of LLMs across five core personality dimensions: Openness, Conscientiousness, Extraversion, Agreeableness, and Neuroticism. Our findings reveal that LLMs exhibit unique dominant traits, varying characteristics, and distinct personality profiles even within the same family of models.
Digital Disparities: A Comparative Web Measurement Study Across Economic Boundaries
Authors: Masudul Hasan Masud Bhuiyan, Matteo Varvello, Cristian-Alexandru Staicu, Yasir Zaki
Venue: WWW
Year: 2025

While internet usage is slowly catching up globally, it is still unclear how the web experience differs in developing and developed countries. On the one hand, the web has a notoriously large inertia, with many webpages still relying on unencrypted HTTP, deprecated web features, or old and buggy libraries. On the other hand, developing countries are expected to leapfrog and directly adopt the newest technologies by learning from the prior mistakes of more developed countries. Anecdotal evidence suggests that webpages in developing and developed regions differ significantly. In this work, we test this hypothesis by measuring differences in web development practices across the two groups of countries, using multiple dimensions: webpages' size, complexity, security, privacy, quality, technology adoption, and accessibility. Concretely, we collect the largest dataset to date that compares web development practices across developed and developing regions -- 200,000 webpages across 20 countries -- which we aim to open source along with this publication. Our findings reveal that webpages in developing regions are generally smaller and simpler, utilizing fewer requests - an adaptation that improves the performance over slower network conditions common in these areas. However, these sites are less optimized in other critical aspects: they frequently employ inefficient image formats, include unnecessary JavaScript or CSS code, or lack responsive image designs. Notably, our security assessment shows developing regions lagging in HTTPS adoption and vulnerability mitigation, possibly due to lower awareness of best practices.
RBDQ: A Reliable LLM-based Text-to-SQL System for Business Data Queries
Authors: Fenglin Bi, Dongdong Cao, Zhiyu Wang, Yang Chen, Fangliang Zhao, Tao Hu, Zhi Li, Yanbin Zhang, Wei Wang
Venue: WWW
Year: 2025

Using large language models (LLMs) to convert natural language (NL) into SQL simplifies data access for users by allowing them to use everyday language. However, business departments often distrust LLM-based text-to-SQL systems due to the probabilistic nature of SQL generation, which can result in incorrect but executable SQL queries caused by model hallucinations. This leads to significant concerns regarding the accuracy and reliability of the queried data. In this paper, we present RBDQ, a novel LLM-based text-to-SQL system designed to address the unique challenges of business data queries. RBDQ innovatively introduces the Hierarchical Metrics Query Method and integrates advanced Retrieval-Augmented Generation (RAG) methods along with a self-reflection mechanism to tackle these challenges. RBDQ effectively meets the requirements of business metric queries in real-world scenarios. Currently implemented in the Quality Assurance department at ByteDance, RBDQ has significantly improved operational efficiency and query flexibility. Our experiments demonstrate the system's effectiveness, achieving an Execution Accuracy of 96.20%.
Selective Multi-grained Alignment for Text-Video Retrieval
Authors: Ziyi Bian, Cong Jiang, Fangzhi Zhu, Zheng Zhang
Venue: WWW
Year: 2025

Multi-grained alignment for text-video retrieval takes advantage of both coarse- and fine-grained semantic alignments, which can enhance the representation capabilities of cross-modal information retrieval. However, the current research fails to fully explore the informative semantics embedded in fine-grained levels of both text and video spaces. In this work, we propose a Selective Multi-Grained Alignment framework, termed SMA, which achieves video-sentence and object-phrase alignments through coarse- and fine-grained alignment modules. Specifically, we introduce a token aggregation module, which implicitly selects important patches per frame and words in text and then aggregates them to generate object-level features for frames and phrase-level features for text. Moreover, we apply a similarity-aware selection module to select keyframes in video efficiently. Extensive experiments on four benchmarks demonstrate that our SMA achieves superior performance on MSR-VTT (52.8%), MSVD (48.2%), ActivityNet (48.9%), and DiDeMo (50.1%). The code of this paper is available at https://github.com/bzy-source/SMA.