OpenRaiser/Intern-Atlas huggingface dataset:
An Investigation into the Feasibility of Performing Federated Learning on Social Linked Data Servers
Authors: Nayil Arana, Mohamed Ragab, Thanassis Tiropanis
Venue: WWW
Year: 2024

Federated Learning (FL) and the Social Linked Data (\textttSolid ~\footnotehttps://solidproject.org/ ) framework represent decentralized approaches to machine learning and web development, respectively, with a focus on preserving privacy. Federated learning enables the distributed training of machine learning models across datasets partitioned across multiple clients, whereas applications developed with the Solid approach store data inPersonal Online Data Stores (pods) under the control of individual users. This paper discusses the merits and challenges of executing Federated Learning on Solid pods and the readiness of the Solid server architecture to support this. We aim to detail these challenges, in addition to identifying avenues for further work to fully harness the benefits of Federated Learning in Solid environments, where users retain sovereignty over their data.
NPCS: Native Provenance Computation for SPARQL
Authors: Zubaria Asma, Daniel Hernández, Luis Galárraga, Giorgos Flouris, Irini Fundulaki, Katja Hose
Venue: WWW
Year: 2024

The popularity of Knowledge Graphs (KGs) both in industry and academia owes credit to their flexible data model, suitable for data integration from multiple sources. Several KG-based applications such as trust assessment or view maintenance on dynamic data rely on the ability to compute provenance explanations for query results. The how-provenance of a query result is an expression that encodes the records (triples or facts) that explain its inclusion in the result set. This article proposes NPCS, a Native Provenance Computation approach for SPARQL queries. NPCS annotates query results with their how-provenance. By building upon spm-provenance semirings, NPCS supports both monotonic and non-monotonic SPARQL queries. Thanks to its reliance on query rewriting techniques, the approach is directly applicable to already deployed SPARQL engines using different reification schemes - including RDF-star. Our experimental evaluation on two popular SPARQL engines (GraphDB and Stardog) shows that our novel query rewriting brings a significant runtime improvement over existing query rewriting solutions, scaling to RDF graphs with billions of triples.
Uncovering the Hidden Data Costs of Mobile YouTube Video Ads
Authors: Emaan Atique, Saad Sher Alam, Harris Ahmad, Ihsan Ayyub Qazi, Zafar Ayyub Qazi
Venue: WWW
Year: 2024

Popular video streaming platforms attract a large number of global marketers who use the platform to advertise their services. While benefiting platforms and advertisers, users are burdened with the costs of advertisements. Users not only pay for these ads with their invested time and personal information, but also through a substantial amount of data translating into direct financial cost. The financial cost becomes even more pronounced in developing countries, where the cost of mobile broadband can be disproportionately high relative to average income levels. In this paper, we perform the first independent and empirical analysis of the data costs of mobile video ads on YouTube, the most popular video platform, from the users' perspective. To do so, we collect and analyze a data set of over 46,000 YouTube video ads. We find that streaming video ads have multiplelatent andavoidable sources of data wastage, which can lead to excessive data consumption by users. We also conduct an affordability analysis to quantify the overall impact of data wastage and reveal the specific data costs per country associated with these losses. Our findings highlight the need for video platform providers, such as YouTube, to minimize data wastage linked to ads, to make their services more affordable and inclusive.
Ducho 2.0: Towards a More Up-to-Date Unified Framework for the Extraction of Multimodal Features in Recommendation
Authors: Matteo Attimonelli, Danilo Danese, Daniele Malitesta, Claudio Pomo, Giuseppe Gassi, Tommaso Di Noia
Venue: WWW
Year: 2024

In this work, we introduce Ducho 2.0, the latest stable version of our framework. Differently from Ducho, Ducho 2.0 offers a more personalized user experience with the definition and import of custom extraction models fine-tuned on specific tasks and datasets. Moreover, the new version is capable of extracting and processing features through multimodal-by-design large models. Notably, all these new features are supported by optimized data loading and storing to the local memory. To showcase the capabilities of Ducho 2.0, we demonstrate a complete multimodal recommendation pipeline, from the extraction/processing to the final recommendation. The idea is to provide practitioners and experienced scholars with a ready-to-use tool that, put on top of any multimodal recommendation framework, may permit them to run extensive benchmarking analyses. All materials are accessible at: https://github.com/sisinflab/Ducho/
The Effect of Alter Ego Accounts on A/B Tests in Social Networks
Authors: Katherine Avery, Amir Houmansadr, David D. Jensen
Venue: WWW
Year: 2024

Social network users often maintain multiple active accounts, sometimes referred to as alter egos. Examples of alter egos include personal and professional accounts or named and anonymous accounts. If alter egos are common on a platform, they can affect the results of A/B testing because a user's alter egos can influence each other. For a single user, one account may be assigned treatment, while another is assigned control. Alter-ego bias is relevant when the treatment affects the individual user rather than the account. Through experimentation and theoretical analysis, we examine the worst and expected case bias for different numbers of alter egos and for a variety of network structures and peer effect strengths. We show that alter egos moderately bias the results of simulated A/B tests on several network structures, including a real-world Facebook subgraph and several types of synthetic networks: small world networks, forest fire networks, stochastic block models, and a worst-case structure. We also show that bias increases with the number of alter egos and that different network structures have different upper bounds on bias.
A Case Study of Enhancing Sparse Retrieval using LLMs
Authors: Michael Antonios Kruse Ayoub, Zhan Su, Qiuchi Li
Venue: WWW
Year: 2024

While dense retrieval methods have made significant advancements, sparse retrieval techniques continue to offer advantages in terms of interpretability and generalizability. However, query-document term mismatch in sparse retrieval persists, rendering it infeasible for many practical applications. Recent research has shown that Large Language Models (LLMs) hold relevant information that can enhance sparse retrieval through the application of prompt engineering. In this paper, we build upon this concept to explore various strategies employing LLMs for information retrieval purposes. Specifically, we utilize LLMs to enhance sparse retrieval by query rewriting and query expansion. In query rewriting, the original query is refined by creating several new queries. For query expansion, LLMs are employed to generate extra terms, thereby enriching the original query. We conduct experiments on a range of well-known information retrieval datasets, including MSMARCO-passage, TREC2019, TREC2020, Natural Questions, SCIFACT. The experiments show that LLMs can be beneficial for sparse methods since the added information provided by the LLMs can help diminish the discrepancy between the term frequencies of the important terms in a query and the relevant document. In certain domains, we demonstrate that the effectiveness of LLMs is constrained, indicating that they may not consistently perform optimally, which will be explored in future research.
Assessing Web Fingerprinting Risk
Authors: Enrico Bacis, Igor Bilogrevic, Róbert Busa-Fekete, Asanka Herath, Antonio Sartori, Umar Syed
Venue: WWW
Year: 2024

Modern Web APIs allow developers to provide extensively customized experiences for website visitors, but the richness of the device information they provide also make them vulnerable to being abused to construct browser fingerprints, device-specific identifiers that enable covert tracking of users even when cookies are disabled. Previous research has established entropy, a measure of information, as the key metric for quantifying fingerprinting risk. However, earlier studies had two major limitations. First, their entropy estimates were based on either a single website or a very small sample of devices. Second, they did not adequately consider correlations among different Web APIs, potentially grossly overestimating their fingerprinting risk. We provide the first study of browser fingerprinting which addresses the limitations of prior work. Our study is based on actual visited pages and Web APIs reported by tens of millions of real Chrome browsers in-the-wild. We accounted for the dependencies and correlations among Web APIs, which is crucial for obtaining more realistic entropy estimates. We also developed a novel experimental design that accurately and efficiently estimates entropy while never observing too much information from any single user. Our results provide an understanding of the distribution of entropy for different website categories, confirm the utility of entropy as a fingerprinting proxy, and offer a method for evaluating browser enhancements which are intended to mitigate fingerprinting.
Item-Ranking Promotion in Recommender Systems
Authors: Hong-Kyun Bae, Hae-Ri Jang, Yang-Sae Moon, Sang-Wook Kim
Venue: WWW
Year: 2024

In this paper, we first define the problem of item-ranking promotion (IRP) in recommender systems as (Goal 1) maintaining a high level of overall recommendation accuracy while (Goal 2) recommending the items with extra values (i.e., RP-items) to as many users as possible. Our novel framework, proposed to address the IRP problem, is based on our own loss function that simultaneously aims to achieve the two goals above and employs a learning-to-rank scheme for training a recommender model. Via extensive experiments, we validate the effectiveness of our framework in terms of the exposure rate of RP-items and the accuracy of recommendation.
Negative Sampling in Next-POI Recommendations: Observation, Approach, and Evaluation
Authors: Hong-Kyun Bae, Yebeen Kim, Hyunjoon Kim, Sang-Wook Kim
Venue: WWW
Year: 2024

To recommend the points of interest (POIs) that a user would check-in next, most deep-learning (DL)-based existing studies have employed random negative (RN) sampling during model training. In this paper, we claim and validate that, as the training proceeds, such an RN sampling in reality performs as sampling easy negative (EN) POIs (i.e., EN sampling) that a user was highly unlikely to check-in at her check-in time point. Furthermore, we verify that EN sampling is more disadvantageous in improving the accuracy than sampling hard negative (HN) POIs (i.e., HN sampling) that a user was highly likely to check-in. To address this limitation, we present the novel concept of the Degree of Positiveness (DoP), which can be formulated by two factors: (i) the degree to which a POI has the characteristics preferred by a user; (ii) the geographical distance between a user and a POI. Then, we propose a new model-training scheme based on HN sampling by using DoP. Using real-world datasets (i.e., NYC, TKY, and Brightkite), we demonstrate that all the state-of-the-art models trained by our scheme showed dramatic improvements in accuracy by up to about 82.8%.
KGQuiz: Evaluating the Generalization of Encoded Knowledge in Large Language Models
Authors: Yuyang Bai, Shangbin Feng, Vidhisha Balachandran, Zhaoxuan Tan, Shiqi Lou, Tianxing He, Yulia Tsvetkov
Venue: WWW
Year: 2024

Large language models (LLMs) demonstrate remarkable performance on knowledge-intensive tasks, suggesting that real-world knowledge is encoded in their model parameters. However, besides explorations on a few probing tasks in limited knowledge domains, it is not well understood how to evaluate LLMs' knowledge systematically and how well their knowledge abilities generalize, across a spectrum of knowledge domains and progressively complex task formats. To this end, we propose KGQuiz, a knowledge-intensive benchmark to comprehensively investigate the knowledge generalization abilities of LLMs. KGQuiz is a scalable framework constructed from triplet-based knowledge, which covers three knowledge domains and consists of five tasks with increasing complexity: true-or-false, multiple-choice QA, blank filling, factual editing, and open-ended knowledge generation. To gain a better understanding of LLMs' knowledge abilities and their generalization, we evaluate 10 open-source and black-box LLMs on the KGQuiz benchmark across the five knowledge-intensive tasks and knowledge domains. Extensive experiments demonstrate that LLMs achieve impressive performance in straightforward knowledge QA tasks, while settings and contexts requiring more complex reasoning or employing domain-specific facts still present significant challenges. We envision KGQuiz as a testbed to analyze such nuanced variations in performance across domains and task formats, and ultimately to understand, evaluate, and improve LLMs' knowledge abilities across a wide spectrum of knowledge domains and tasks.
End-to-End Graph-Sequential Representation Learning for Accurate Recommendations
Authors: Vladimir Baikalov, Evgeny Frolov
Venue: WWW
Year: 2024

Recent recommender system advancements have focused on developing sequence-based and graph-based approaches. Both approaches proved useful in modeling intricate relationships within behavioral data, leading to promising outcomes in personalized ranking and next-item recommendation tasks while maintaining good scalability. However, they capture very different signals from data. While the former approach represents users directly through ordered interactions with recent items, the latter aims to capture indirect dependencies across the interactions graph. This paper presents a novel multi-representational learning framework exploiting these two paradigms' synergies. Our empirical evaluation on several datasets demonstrates that mutual training of sequential and graph components with the proposed framework significantly improves recommendations performance.
Diagrammatic Reasoning for ALC Visualization with Logic Graphs
Authors: Ildar Baimuratov
Venue: WWW
Year: 2024

User studies show the demand for diagrammatic reasoning techniques for knowledge representation formats. OWL ontologies are highly relevant for Web 3.0, however, existing ontology visualization tools do not support diagrammatic reasoning, while existing diagrammatic reasoning systems utilize suboptimal visual languages. The purpose of this research is to facilitate the usage of OWL ontologies by providing a diagrammatic reasoning system over their visual representations. We focus on the ALC description logic, which covers most of the expressivity of the ontologies. As a visual language to reason about, we utilize Logic Graphs, which provide the simplest visualizations regarding graph- and information-theoretic properties. We adapt the tableau algorithm to LGs to reason about concept satisfiability, prove the correctness of the proposed system and illustrate it with examples. The proposed diagrammatic reasoning system allows reasoning over ontologies, reducing complex concepts step by step, and identifying elements that produce a contradiction.
Tutorial on User Simulation for Evaluating Information Access Systems on the Web
Authors: Krisztian Balog, ChengXiang Zhai
Venue: WWW
Year: 2024

Users routinely interact with the Web via information access systems such as search engines and recommender systems. How to accurately evaluate such interactive systems with reproducible experiments is an important, yet difficult challenge. To address this challenge, user simulation has emerged as a promising solution. This half-day tutorial focuses on providing a thorough introduction to user simulation techniques designed specifically for evaluating information access systems on the Web. We systematically review major research progress, covering both general frameworks for designing user simulators, and specific models and algorithms for simulating user interactions with search engines, recommender systems, and conversational assistants. We also highlight some important future research directions.
A Counterfactual Framework for Learning and Evaluating Explanations for Recommender Systems
Authors: Oren Barkan, Veronika Bogina, Liya Gurevitch, Yuval Asher, Noam Koenigstein
Venue: WWW
Year: 2024

In the field of recommender systems, explainability remains a pivotal yet challenging aspect. To address this, we introduce the Learning to eXplain Recommendations (LXR) framework, a post-hoc, model-agnostic approach designed for providing counterfactual explanations. LXR is compatible with any differentiable recommender algorithm and scores the relevance of user data in relation to recommended items. A distinctive feature of LXR is its use of novel self-supervised counterfactual loss terms, which effectively highlight the most influential user data responsible for a specific recommended item. Additionally, we propose several innovative counterfactual evaluation metrics specifically tailored for assessing the quality of explanations in recommender systems. Our code is available on our GitHub repository: https://github.com/DeltaLabTLV/LXR.
PKG API: A Tool for Personal Knowledge Graph Management
Authors: Nolwenn Bernard, Ivica Kostric, Weronika Lajewska, Krisztian Balog, Petra Galuscáková, Vinay Setty, Martin G. Skjæveland
Venue: WWW
Year: 2024

Personal knowledge graphs (PKGs) offer individuals a way to store and consolidate their fragmented personal data in a central place, improving service personalization while maintaining full user control. Despite their potential, practical PKG implementations with user-friendly interfaces remain scarce. This work addresses this gap by proposing a complete solution to represent, manage, and interface with PKGs. Our approach includes (1) a user-facing PKG Client, enabling end-users to administer their personal data easily via natural language statements, and (2) a service-oriented PKG API. To tackle the complexity of representing these statements within a PKG, we present an RDF-based PKG vocabulary that supports this, along with properties for access rights and provenance.
Social Psychology Meets Social Computing: State of the Art and Future Directions
Authors: Sourav S. Bhowmick, Hui Li, S. H. Annabel Chen, Yining Zhao
Venue: WWW
Year: 2024

Social computing platforms typically deal with data that are either related to humans or generated by humans. Consequently, effective design of these platforms needs to be cognizant ofsocial psychology theories. In this tutorial, we review and summarize the research thus far into the paradigm ofpsychology theory-informed design of social computing platforms where the design is guided by theories from social psychology in addition to theories from computer science. Specifically, we review techniques and frameworks that embrace this paradigm in the arena of social influence. In addition, we suggest open problems and new research directions.
FusionRender: Harnessing WebGPU's Power for Enhanced Graphics Performance on Web Browsers
Authors: Weichen Bi, Yun Ma, Yudong Han, Yifan Chen, Deyu Tian, Jiaqi Du
Venue: WWW
Year: 2024

Graphics rendering on web browsers serves as the foundation for numerous web applications. Compared with the widely employed WebGL, the next-generation web graphics API, WebGPU, demonstrates an enhanced capacity to adapt to modern GPU features, boasting more significant potential. However, our experiment shows that the performance of current graphics rendering frameworks based on WebGPU lags behind those built on WebGL. Such discrepancy primarily arises from an incomplete alignment with WebGPU's distinctive features. The individual rendering of each graphic leads to redundant communication between the CPU and GPU. To enhance the graphics performance on the web, we introduce the FusionRender to harness the power of WebGPU. To mitigate redundant communication, FusionRender assigns a unique signature to each object and employs these signatures for grouping, enabling the consolidation of graphics rendering whenever possible. In simulated experiments involving the rendering of multiple objects, FusionRender improves the rendering performance by 29.3%-122.1% compared with the existing optimal baseline. In real cases with more complex features, performance improvement ranges from 9.4% to 39.7%. Additionally, FusionRender exhibits robust performance enhancement across various devices and browsers.
Causal Question Answering with Reinforcement Learning
Authors: Lukas Blübaum, Stefan Heindorf
Venue: WWW
Year: 2024

Causal questions inquire about causal relationships between different events or phenomena. They are important for a variety of use cases, including virtual assistants and search engines. However, many current approaches to causal question answering cannot provide explanations or evidence for their answers. Hence, in this paper, we aim to answer causal questions with a causality graph, a large-scale dataset of causal relations between noun phrases along with the relations' provenance data. Inspired by recent, successful applications of reinforcement learning to knowledge graph tasks, such as link prediction and fact-checking, we explore the application of reinforcement learning on a causality graph for causal question answering. We introduce an Actor-Critic-based agent which learns to search through the graph to answer causal questions. We bootstrap the agent with a supervised learning procedure to deal with large action spaces and sparse rewards. Our evaluation shows that the agent successfully prunes the search space to answer binary causal questions by visiting less than 30 nodes per question compared to over 3,000 nodes by a naive breadth-first search. Our ablation study indicates that our supervised learning strategy provides a strong foundation upon which our reinforcement learning agent improves. The paths returned by our agent explain the mechanisms by which a cause produces an effect. Moreover, for each edge on a path, our causality graph provides its original source allowing for easy verification of paths.
Hierarchical Tensor Clustering for Multiple Graphs Representation
Authors: Karima Boutalbi, Rafika Boutalbi, Hervé Verjus, Kavé Salamatian
Venue: WWW
Year: 2024

Graph clustering is a challenging task, especially when there is a hierarchical structure. The availability of multiple graphs (or relational graphs), in the multi-graph setting, provides additional information that can be leveraged to improve clustering results. This paper aims to develop a new hierarchical clustering algorithm for multi-graphs, the HTGM algorithm. This algorithm represents the set of graphs in the multi-graph as a 3-way tensor, and maximizes a modularity measure, extending the modularity-based graph clustering algorithm to multi-graphs and tensor structures. We evaluate the proposed algorithm over synthetic and real-world datasets and show the effectiveness of the proposed algorithm by benchmarking it to alternative clustering algorithms.