OpenRaiser/Intern-Atlas huggingface dataset:
Enhancing E-commerce Representation Learning via Hypergraph Contrastive Learning and Interpretable LLM-Driven Analysis
Authors: Yiyue Qian, Shinan Zhang, Lanhao Chen, Diego Socolinsky, Negin Sokhandan, Song Cui, De Chen, Suchitra Sathyanarayana
Venue: WWW
Year: 2025

E-commerce has experienced significant growth recently, generating vast amounts of data on user preferences, interactions, and purchase patterns. Effectively modeling and representing users and products in these online ecosystems is crucial for various applications. However, existing approaches for e-commerce representation learning face several limitations: (i) they primarily consider user behavior patterns while ignoring rich group-wise relationships; (ii) some works focus on either user or product representations, failing to learn both simultaneously; (iii) results on downstream tasks are generated by ''black box'' models, making it difficult to interpret prediction results. To address these challenges, we propose RelationExpert, a general e-commerce representation learning framework. It consists of two components: RelationEmbed, an e-commerce representation learning foundation model, and TaskReport, an interpretability-driven LLM module. RelationEmbed is a self-supervised hypergraph contrastive learning model to capture multi-modal features and rich group-wise relationships among unlabeled data, i.e., merchants, customers, and products. TaskReport generates interpretable reports that explain the results of downstream tasks utilizing RelationEmbed's learned embeddings. As a result, (i) General: RelationExpert is applicable to various e-commerce related tasks; (ii) Novel and Powerful: as the first e-commerce hypergraph contrastive learning framework, RelationEmbed significantly outperforms existing methods across eight downstream tasks on two markets; (iii) Interpretable and Reliable: TaskReport provides clear insights into ''black box'' results and delivers reliable reports with high factuality and clarity.
Hyperbolic Multi-semantic Transition for Next POI Recommendation
Authors: Hongliang Qiao, Shanshan Feng, Min Zhou, Wentao Li, Fan Li
Venue: WWW
Year: 2025

The next Point-of-Interest (POI) recommendation has gained significant research interest, focusing on learning users' mobility patterns from sparse check-in data. Existing POI recommendation models face two main constraints. First, most models are based on Euclidean space and struggle with capturing the inherent hierarchical structures in historical check-ins. Second, various transition semantics in both one-hop and sequential transitions cannot be properly utilized to understand user movement trends. To overcome the above limitations, we introduce rotation operations in hyperbolic space, enabling the joint modeling of hierarchical structures and various transition semantics to effectively capture complex mobility patterns. Specifically, a novel hyperbolic rotation-based recommendation model HMST is developed for the next POI recommendation. To our knowledge, this is the first work to explore the hyperbolic rotations for the next POI recommendation tasks. Extensive experiments on three real-world datasets demonstrate the superiority of our proposed approach over the various state-of-the-art baselines.
Thematic-LM: A LLM-based Multi-agent System for Large-scale Thematic Analysis
Authors: Tingrui Qiao, Caroline Walker, Chris Cunningham, Yun Sing Koh
Venue: WWW
Year: 2025

Thematic analysis (TA) is a widely used qualitative method for identifying underlying meanings within unstructured text. However, TA requires manual processes, which become increasingly labour-intensive and time-consuming as datasets grow. While large language models (LLMs) have been introduced to assist with TA on small-scale datasets, three key limitations hinder their effectiveness. First, current approaches often depend on interactions between an LLM agent and a human coder, a process that becomes challenging with larger datasets. Second, with feedback from the human coder, the LLM tends to mirror the human coder, which provides a narrower viewpoint of the data. Third, existing methods follow a sequential process, where codes are generated for individual samples without recalling previous codes and associated data, reducing the ability to analyse data holistically. To address these limitations, we propose Thematic-LM, an LLM-based multi-agent system for large-scale computational thematic analysis. Thematic-LM assigns specialised tasks to each agent, such as coding, aggregating codes, and maintaining and updating the codebook. We assign coder agents different identity perspectives to simulate the subjective nature of TA, fostering a more diverse interpretation of the data. We applied Thematic-LM to the Dreaddit dataset and the Reddit climate change dataset to analyse themes related to social media stress and online opinions on climate change. We evaluate the resulting themes based on trustworthiness principles in qualitative research. Our study reveals insights such as assigning different identities to coder agents promotes divergence in codes and themes.
D2K: Turning Historical Data into Retrievable Knowledge for Recommender Systems
Authors: Jiarui Qin, Weiwen Liu, Weinan Zhang, Yong Yu
Venue: WWW
Year: 2025

A vast amount of user behavior data is constantly accumulating on today's large recommendation platforms, recording users' various interests and tastes. Preserving knowledge from the old data while new data continually arrives is a vital problem for recommender systems. Existing approaches generally seek to save the knowledge implicitly in the model parameters. However, such a parameter-centric approach lacks scalability and flexibility---the capacity is hard to scale, and the knowledge is inflexible to utilize. Hence, in this work, we propose a framework that turns massive user behavior data to retrievable knowledge (D2K). It is a data-centric approach that is model-agnostic and easy to scale up. Different from only storing unary knowledge such as the user-side or item-side information, D2K propose to store ternary knowledge for recommendation, which is determined by the complete recommendation factors---user, item, and context. The knowledge retrieved by target samples can be directly used to enhance the performance of any recommendation algorithms. Specifically, we introduce a Transformer-based knowledge encoder to transform the old data into knowledge with the user-item-context cross features. A personalized knowledge adaptation unit is devised to effectively exploit the information from the knowledge base by adapting the retrieved knowledge to the target samples. Extensive experiments demonstrate the superiority of the proposed method.
GreenBites: A Real-Time Diet Assistant for Sustainable Health and Eco-Friendly Living
Authors: Yang Qiu, Yinan Zhang, Hongyu Zhou, Bo Huang, Zhiqi Shen
Venue: WWW
Year: 2025

Living a sustainable life requires being both environmentally aware and health-conscious. While sustainability is increasingly promoted in the food industry, gaps in information accessibility contribute significantly to the disparity between consumers' sustainable purchasing intentions and their actual behavior. Furthermore, environmental sustainability and health-conscious choices are often promoted separately, making it challenging for consumers to address both aspects at the same time. To bridge these gaps, we present GreenBites, a real-time diet assistant designed to integrate health and environmental impact assessments at the point of purchase, assisting consumers in making more informed and sustainable food choices. GreenBites leverages multimodal inputs, food recognition powered by Large Language Models (LLMs), and advanced algorithms to analyze food items and evaluate them across multiple indices, including glycemic impact, calorie content, nutrition, and environmental sustainability. By delivering assessments and recommendations in an accessible and convenient format, GreenBites empowers users to make decisions that simultaneously support their health and promote environmental sustainability.
Evolving Skeletons: Motion Dynamics in Action Recognition
Authors: Jushang Qiu, Lei Wang
Venue: WWW
Year: 2025

Skeleton-based action recognition has gained significant attention for its ability to efficiently represent spatiotemporal information in a lightweight format. Most existing approaches use graph-based models to process skeleton sequences, where each pose is represented as a skeletal graph structured around human physical connectivity. Among these, the Spatiotemporal Graph Convolutional Network (ST-GCN) has become a widely used framework. Alternatively, hypergraph-based models, such as the Hyperformer, capture higher-order correlations, offering a more expressive representation of complex joint interactions. A recent advancement, termed Taylor Videos, introduces motion-enhanced skeleton sequences by embedding motion concepts, providing a fresh perspective on interpreting human actions in skeleton-based action recognition. In this paper, we conduct a comprehensive evaluation of both traditional skeleton sequences and Taylor-transformed skeletons using ST-GCN and Hyperformer models on the NTU-60 and NTU-120 datasets. We compare skeletal graph and hypergraph representations, analyzing static poses against motion-injected poses. Our findings highlight the strengths and limitations of Taylor-transformed skeletons, demonstrating their potential to enhance motion dynamics while exposing current challenges in fully using their benefits. This study underscores the need for innovative skeletal modeling techniques to effectively handle motion-rich data and advance the field of action recognition.
A Unified CatBoost Framework for IoT Data Classification Using Multi-Level Feature Extraction
Authors: Chengfeng Qiu, Jiahui Zhou, Yongfeng Liao, Zhengliang Cui, Dan Li
Venue: WWW
Year: 2025

Buildings are major energy consumers, and efficient management is crucial for reducing emissions, yet the lack of standardised data formats complicates this process. This paper presents a method for automating the classification of time-series building data, using a segmentation approach to enhance feature extraction and applying hierarchical differential features to capture temporal dynamics. The multi-label classification problem is reformulated into a unified single-label classification task by encoding multiple labels into a discrete 91-class label space. The model, trained with CatBoost and evaluated using 5-fold cross-validation, secured a top ranking in the Brick by Brick 2024 competition, demonstrating its effectiveness in promoting energy-efficient building operations.
TensorJSFuzz: Effective Testing of Web-Based Deep Learning Frameworks via Input-Constraint Extraction
Authors: Lili Quan, Xiaofei Xie, Qianyu Guo, Lingxiao Jiang, Sen Chen, Junjie Wang, Xiaohong Li
Venue: WWW
Year: 2025

As web applications grow in popularity, developers are increasingly integrating deep learning (DL) models into these environments. Web-based DL frameworks (e.g., TensorFlow.js) are essential for building and deploying such applications. Therefore, ensuring the quality of these frameworks is critical. While extensive testing efforts have been made for native DL frameworks such as TensorFlow and PyTorch, web-based DL frameworks have not yet undergone systematic testing. A key challenge is generating syntactically and semantically valid inputs while designing effective test oracles for web environments. To address this, we introduce TensorJSFuzz, a novel method for testing web-based DL frameworks. To ensure input quality, TensorJSFuzz extracts constraints directly from the source code of DL operators. By leveraging Large Language Models (e.g., ChatGPT) to understand the code and extract input constraints, TensorJSFuzz performs type-aware random generation coupled with dependency-aware refinement to create high-quality test inputs. These inputs are then subjected to differential testing across various backends, including CPU, TensorFlow, Wasm, and WebGL. Our experimental results show that TensorJSFuzz outperforms all baselines in generating valid inputs and identifying bugs. In particular, TensorJSFuzz successfully detected 92 bugs, with 30 already confirmed or fixed by developers, demonstrating its effectiveness in improving the robustness of web-based DL frameworks.
Bridging Conversational and Collaborative Signals for Conversational Recommendation
Authors: Ahmad Bin Rabiah, Nafis Sadeq, Julian J. McAuley
Venue: WWW
Year: 2025

Conversational recommendation systems (CRS) leverage contextual information from conversations to generate recommendations but often struggle due to a lack of collaborative filtering (CF) signals, which capture user-item interaction patterns essential for accurate recommendations. We introduce Reddit-ML32M, a dataset that links Reddit conversations with interactions on MovieLens 32M, to enrich item representations by leveraging collaborative knowledge and addressing interaction sparsity in conversational datasets. We propose an LLM-based framework that uses Reddit-ML32M to align LLM-generated recommendations with CF embeddings, refining rankings for better performance. We evaluate our framework against three sets of baselines: CF-based recommenders using only interactions from CRS tasks, traditional CRS models, and LLM-based methods relying on conversational context without item representations. Our approach achieves consistent improvements, including a 12.32% increase in Hit Rate and a 9.9% improvement in NDCG, outperforming the best-performing baseline that relies on conversational context but lacks collaborative item representations.
SynDL: A Large-Scale Synthetic Test Collection for Passage Retrieval
Authors: Hossein A. Rahmani, Xi Wang, Emine Yilmaz, Nick Craswell, Bhaskar Mitra, Paul Thomas
Venue: WWW
Year: 2025

Large-scale test collections play a crucial role in Information Retrieval (IR) research. However, according to the Cranfield paradigm and the research into publicly available datasets, the existing information retrieval research studies are commonly developed on small-scale datasets that rely on human assessors for relevance judgments - a time-intensive and expensive process. Recent studies have shown the strong capability of Large Language Models (LLMs) in producing reliable relevance judgments with human accuracy but at a greatly reduced cost. In this paper, to address the missing large-scale ad-hoc document retrieval dataset, we extend the TREC Deep Learning Track (DL) test collection via additional language model synthetic labels to enable researchers to test and evaluate their search systems at a large scale. Specifically, such a test collection includes more than 1,900 test queries from the previous years of tracks. We compare system evaluation with past human labels from past years and find that our synthetically created large-scale test collection can lead to highly correlated system rankings.
JudgeBlender: Ensembling Automatic Relevance Judgments
Authors: Hossein A. Rahmani, Emine Yilmaz, Nick Craswell, Bhaskar Mitra
Venue: WWW
Year: 2025

The effective training and evaluation of retrieval systems require a substantial amount of relevance judgments, which are traditionally collected from human assessors - a process that is both costly and time-consuming. Large Language Models (LLMs) have shown promise in generating relevance labels for search tasks, offering a potential alternative to manual assessments. Current approaches often rely on a single LLM, such as GPT-4, which, despite being effective, are expensive and prone to intra-model biases that can favour systems leveraging similar models. In this work, we introduce JudgeBlender, a framework that employs smaller, open-source models to provide relevance judgments by combining evaluations across multiple LLMs (LLMBlender) or multiple prompts (Prompt-Blender). By leveraging the LLMJudge benchmark [10], we compare JudgeBlender with state-of-the-art methods and the top performers in the LLMJudge challenge. Our results show that JudgeBlender achieves competitive performance, demonstrating that very large models are often unnecessary for reliable relevance assessments.
BERTDetect: A Neural Topic Modelling Approach for Android Malware Detection
Authors: Nishavi Ranaweera, Jiarui Xu, Suranga Seneviratne, Aruna Seneviratne
Venue: WWW
Year: 2025

Web access today occurs predominantly through mobile devices, with Android representing a significant share of the mobile device market. This widespread usage makes Android a prime target for malicious attacks. Despite efforts to combat malicious attacks through tools like Google Play Protect and antivirus software, new and evolved malware continues to infiltrate Android devices. Source code analysis is effective but limited, as attackers quickly abandon old malware for new variants to evade detection. Therefore, there is a need for alternative methods that complement source code analysis. Prior research investigated clustering applications based on their descriptions and identified outliers in these clusters by API usage as malware. However, these works often used traditional techniques such as Latent Dirichlet Allocation (LDA) and k-means clustering, that do not capture the nuanced semantic structures present in app descriptions. To this end, in this paper, we propose BERTDetect, which leverages the BERTopic neural topic modelling to effectively capture the latent topics in app descriptions. The resulting topic clusters are comparatively more coherent than previous methods and represent the app functionalities well. Our results demonstrate that BERTDetect outperforms other baselines, achieving ~10% relative improvement in F1 score.
Self-Comparison for Dataset-Level Membership Inference in Large (Vision-)Language Model
Authors: Jie Ren, Kangrui Chen, Chen Chen, Vikash Sehwag, Yue Xing, Jiliang Tang, Lingjuan Lyu
Venue: WWW
Year: 2025

Large Language Models (LLMs) and Vision-Language Models (VLMs) have made significant advancements in a wide range of natural language processing and vision-language tasks. Access to large web-scale datasets has been a key factor in their success. However, concerns have been raised about the unauthorized use of copyrighted materials and potential copyright infringement. Existing methods, such as sample-level Membership Inference Attacks (MIA) and distribution-based dataset, inference distinguish member and non-member data by leveraging the common observation that models tend to memorize and show greater confidence in member data. Nevertheless, these methods face challenges when applied to LLMs and VLMs, such as the requirement for ground-truth member data or non-member data that shares the same distribution as the test data. In this paper, we propose a novel dataset-level membership inference method based on Self-Comparison. We find that a member prefix followed by a non-member suffix (paraphrased from a member suffix) can further trigger the model's memorization on training data. Instead of directly comparing member and non-member data, we introduce paraphrasing to the second half of the sequence and evaluate how the likelihood changes before and after paraphrasing. Unlike prior approaches, our method does not require access to ground-truth member data or non-member data in identical distribution, making it more practical. Extensive experiments demonstrate that our proposed method outperforms traditional MIA and dataset inference techniques across various datasets and models, including GPT-4o.
PipeMind: Toward a Multi-Agent Framework for Real-Time Feedback and Continuous Optimization in Analytics Pipelines
Authors: Hirad Baradaran Rezaei, Fethi Rabhi, Amin Beheshti
Venue: WWW
Year: 2025

Modern organizations depend on analytics pipelines for decision-making, yet integrating advanced ML/AI techniques often increases complexity and hinders optimal performance. This paper introduces PipeMind, a multi-agent framework that leverages large language models (LLMs) and specialized agents to provide real-time feedback and continuous optimization. Focusing on execution time, resource utilization, and throughput, PipeMind autonomously monitors pipeline performance, identifies bottlenecks, and recommends improvements. Its web-based infrastructure, built on RESTful APIs, seamlessly integrates with external data sources and cloud services. We demonstrate a working prototype with quantitative evaluations and a high-frequency trading case study, underscoring PipeMind's potential to enable fully autonomous analytics pipeline management.
Evaluating Llama 3 for Text Simplification: A Study on Wikipedia Lead Sections
Authors: José Frederico Rodrigues, Henrique Lopes Cardoso, Carla Teixeira Lopes
Venue: WWW
Year: 2025

Text simplification converts complex text into simpler language, improving readability and comprehension. This study evaluates the effectiveness of open-source large language models for text simplification across various categories. We created a dataset of 66,620 lead section pairs from English and Simple English Wikipedia, spanning nine categories, and tested Llama 3 for text simplification. We assessed its output for readability, simplicity, and meaning preservation. Results show improved readability, with simplification varying by category. Texts on Time were the most shortened, while Leisure-related texts had the greatest reduction of words/characters and syllables per sentence. Meaning preservation was most effective for the Objects and Education categories.
Video-Enriched Retrieval Augmented Generation Using Aligned Video Captions
Authors: Kevin Dela Rosa
Venue: WWW
Year: 2025

In this work, we propose the use of ''aligned video captions'' as an intelligent mechanism for integrating video content into retrieval augmented generation (RAG) based AI assistant systems. These captions serve as an efficient representation layer between videos and large language models (LLMs), describing both visual and audio content while requiring significantly less context window space compared to traditional frame sampling approaches. We demonstrate how this representation enables more effective agent-based retrieval and generation capabilities, with captions that can be dynamically adapted through targeted prompting or fine-tuning of the underlying models. Our empirical evaluation across multiple LLM configurations shows that this approach achieves comparable performance to direct video processing while being more computationally efficient and easier to reason about in downstream tasks. Notably, the approach shows particular strength in procedural content like How-To videos, where aligned captions significantly outperform speech-only baselines.
Alan's Speakeasy - An Ecosystem for the Evaluation of Conversational Agents
Authors: Luca Rossetto, Cristina Sarasua, Oana Inel, Juan Diego Bermeo, Pablo Sebastian Bolaños, Hugues Devimeux, Huiran Duan, Robin Hany, Athina Kyriakou, Dhivyabharathi Ramasamy, Varun Ghat Ravikumar, Konstantina Timoleon, Rosni Vasu, Ruijie Wang, Daan van der Weijden, Jan Willi, Abraham Bernstein
Venue: WWW
Year: 2025

Conversational artificially intelligent agents have received increasing attention in recent years. For many use cases, evaluating such agents requires a human-in-the-loop approach. In this demo paper, we present Alan's Speakeasy, a Web-based ecosystem for the evaluation of conversational agents.