OpenRaiser/Intern-Atlas huggingface dataset:
Span-Pair Interaction and Tagging for Dialogue-Level Aspect-Based Sentiment Quadruple Analysis
Authors: Changzhi Zhou, Zhijing Wu, Dandan Song, Linmei Hu, Yuhang Tian, Jing Xu
Venue: WWW
Year: 2024

The Dialogue-level Aspect-based Sentiment Quadruple analysis (DiaASQ) task has recently received attention in the Aspect-Based Sentiment Analysis (ABSA) field. It aims to extract(target, aspect, opinion, sentiment) quadruples from multi-turn and multi-party dialogues. Compared to previous ABSA tasks focusing on text such as sentences, the DiaASQ task involves more complex contextual information and corresponding relations between terms, as well as longer sequences. These characteristics challenge existing methods that struggle to model explicit span-level interactions or have high computational costs. In this paper, we propose a span-pair interaction and tagging method to solve these issues, which includes a novel Span-pair Tagging Scheme (STS) and a simple and efficient Multi-level Representation Model (MRM). STS simplifies the DiaASQ task to a span-pair tagging task and explicitly captures complete span-level semantics by tagging span pairs. MRM efficiently models the dialogue structure information and span-level interactions by constructing multi-level contextual representation. Besides, we train a span ranker to improve the running efficiency of MRM. Extensive experiments on multilingual datasets demonstrate that our method outperforms existing state-of-the-art methods.
Towards Understanding Crypto-Asset Risks on Ethereum Caused by Key Leakage on the Internet
Authors: Yuxuan Zhou, Jiaqi Chen, Yibo Wang, Yuzhe Tang, Guofei Gu
Venue: WWW
Year: 2024

In public blockchains, leaking secret keys can cause the permanent loss of crypto assets. It is imperative to understand the illicit activities on blockchains related to leaked keys. This paper presents the first measurement study that uncovers, quantifies, and characterizes the actual misuses of the leaked keys from top websites on the Internet to withdraw assets on Ethereum. By finding key-leaking web pages and joining them with transactions, the study reveals 7.29*10^6/0.59*10^6 USD worth of assets on Ethereum mainnet/Binance Smart Chain (BSC) are withdrawn from 1421/1514 leaked secret keys. Mitigations are proposed to avoid the financial loss caused by leaked keys.
A Cross Domain Method for Customer Lifetime Value Prediction in Supply Chain Platform
Authors: Zhiyuan Zhou, Li Lin, Hai Wang, Xiaolei Zhou, Gong Wei, Shuai Wang
Venue: WWW
Year: 2024

Accurate customer LifeTime Value (LTV) predictions are crucial for customer relationship management, especially in Supply Chain Platforms (SCP), which involve effectively managing the service resources in business decision-making. Previous LTV prediction methods usually rely on ample historical customer data, which is not available in the early stages of a customer's lifecycle. It makes the modeling of the historical customer data a difficult task due to the data sparsity. Besides, the long-tail distribution of customer LTV also brings new challenges to the prediction of LTV. To tackle the above issues, we propose CDLtvS, a novel Cross Domain method for customer Lifetime value prediction in SCP. It leverages rich cross-domain information from upstream platforms to enhance LTV predictions in downstream platforms. Firstly, CDLtvS pre-trains the customer representations by an LTV modeling framework named LtvS in source and target domains separately. Specifically, LtvS incorporates the Expert Mask Network (ExMN), which not only effectively models the long-tail distribution of LTV in single-domain but also resolves cross-domain learning model bias resulting from this distribution. Then, the various-level alignment mechanism is introduced to keep the consistency of knowledge transferring from source to target domains on both sparse and non-sparse data. Comprehensive experiments on real-world data from JD, one of the world's largest supply chain platforms, demonstrate that CDLtvS achieves a normalized mean average error of 0.3378 in LTV prediction, outperforming 16.3% to the baseline. Additionally, the improvements of ≥2.3% across various data sparsity levels (0% -- 80%) provide valuable insights into cross-domain LTV modeling.
Temporal Interest Network for User Response Prediction
Authors: Haolin Zhou, Junwei Pan, Xinyi Zhou, Xihua Chen, Jie Jiang, Xiaofeng Gao, Guihai Chen
Venue: WWW
Year: 2024

User response prediction is essential in industrial recommendation systems, such as online display advertising. Among all the features in recommendation models, user behaviors are among the most critical. Many works have revealed that a user's behavior reflects her interest in the candidate item, owing to the semantic or temporal correlation between behaviors and the candidate. While the literature has individually examined each of these correlations, researchers have yet to analyze them in combination, that is, the semantic-temporal correlation. We empirically measure this correlation and observe intuitive yet robust patterns. We then examine several popular user interest models and find that, surprisingly, none of them learn such correlation well. To fill this gap, we propose a Temporal Interest Network (TIN) to capture the semantic-temporal correlation simultaneously between behaviors and the target. We achieve this by incorporating target-aware temporal encoding, in addition to semantic encoding, to represent behaviors and the target. Furthermore, we conduct explicit 4-way interaction by deploying target-aware attention and target-aware representation to capture both semantic and temporal correlation. We conduct comprehensive evaluations on two popular public datasets, and our proposed TIN outperforms the best-performing baselines by 0.43% and 0.29% on GAUC, respectively. During online A/B testing in Tencent's advertising platform, TIN achieves 1.65% cost lift and 1.93% GMV lift over the base model. It has been successfully deployed in production since October 2023, serving the WeChat Moments traffic. We have released our code at https://github.com/zhouxy1003/TIN.
Retrieval-augmented Query Reformulation for Heterogeneous Research Asset Retrieval in Virtual Research Environment
Authors: Peide Zhu, Na Li, Zhiming Zhao
Venue: WWW
Year: 2024

Discovering and reusing research assets such as datasets and computational notebooks is crucial for building research workflows in data-centric studies. The rapid growth of research assets in scientific communities provides scientists with great opportunities to enhance research efficacy but also poses significant challenges in finding suitable materials for specific tasks. Scientists, especially those focusing on cross-disciplinary research, often find it difficult to formulate effective queries to retrieve desired resources. Previous work has proposed query reformulation methods to increase the efficiency of research asset search. However, it relies on existent knowledge graphs and is constrained to computational notebooks only. As research assets utilized by data analytic workflows are in essence heterogeneous, i.e., of distinct kinds and from diversified sources, query reformulation methods in this regard should consider the relationship between different types of research assets. To address the above challenges, we propose a retrieval-augmented query reformulation method for heterogeneous research asset retrieval. It is developed in the context of a Notebook-based virtual research environment (VRE) and offers query reformulation services to other VRE components. We demonstrate the effectiveness of the proposed query reformulation service with experiments on dataset and notebook retrieval. Up till now, we have indexed 8,954 datasets and 18,158 notebooks. The experimental results show that the proposed service can create useful query suggestions.
Fair Graph Representation Learning via Sensitive Attribute Disentanglement
Authors: Yuchang Zhu, Jintang Li, Zibin Zheng, Liang Chen
Venue: WWW
Year: 2024

Group fairness for Graph Neural Networks (GNNs), which emphasizes algorithmic decisions neither favoring nor harming certain groups defined by sensitive attributes (e.g., race and gender), has gained considerable attention. In particular, the objective of group fairness is to ensure that the decisions made by GNNs are independent of the sensitive attribute. To achieve this objective, most existing approaches involve eliminating sensitive attribute information in node representations or algorithmic decisions. However, such ways may also eliminate task-related information due to its inherent correlation with the sensitive attribute, leading to a sacrifice in utility. In this work, we focus on improving the fairness of GNNs while preserving task-related information and propose a fair GNN framework named FairSAD. Instead of eliminating sensitive attribute information, FairSAD enhances the fairness of GNNs via Sensitive Attribute Disentanglement (SAD), which separates the sensitive attribute-related information into an independent component to mitigate its impact. Additionally, FairSAD utilizes a channel masking mechanism to adaptively identify the sensitive attribute-related component and subsequently decorrelates it. Overall, FairSAD minimizes the impact of the sensitive attribute on GNN outcomes rather than eliminating sensitive attributes, thereby preserving task-related information associated with the sensitive attribute. Furthermore, experiments conducted on several real-world datasets demonstrate that FairSAD outperforms other state-of-the-art methods by a significant margin in terms of both fairness and utility performance. Our source code is available at https://github.com/ZzoomD/FairSAD.
Improving Prostate Cancer Risk Prediction through Partial AUC Optimization
Authors: Xinyuan Zhu, Xiaohan Ren, Wentao Shi, Changming Wang, Xuehan Liu, Yuqing Liu, Tao Tao, Fuli Feng
Venue: WWW
Year: 2024

Prostate cancer risk prediction (PCRP) is crucial in guiding clinical decision-making and ensuring accurate diagnoses. The area under the receiver operating characteristic curve (AUC) is typically used for the evaluation of PCRP models. However, AUC considers regions with high false positive rates (FPRs), which are not applicable in clinical practice. To address this concern, we propose to use partial AUC (pAUC) as a more clinically meaningful metric which evaluates PCRP models with restricted FPR. Moreover, we propose a new PCRP framework named pAUCP, which optimizes pAUC to train PCRP models and adopts model ensemble to further enhance its usability. We construct clinical datasets obtained from two medical centers over an extended period to evaluate the proposed pAUCP framework. Extensive experiments demonstrate the rationality and superiority of the pAUCP framework, especially the cross-time and cross-center transferability of the obtained PCRP model.
Collaborative Large Language Model for Recommender Systems
Authors: Yaochen Zhu, Liang Wu, Qi Guo, Liangjie Hong, Jundong Li
Venue: WWW
Year: 2024

Recently, there has been growing interest in developing the next-generation recommender systems (RSs) based on pretrained large language models (LLMs). However, the semantic gap between natural language and recommendation tasks is still not well addressed, leading to multiple issues such as spuriously correlated user/item descriptors, ineffective language modeling on user/item data, inefficient recommendations via auto-regression, etc. In this paper, we propose CLLM4Rec, the first generative RS that tightly integrates the LLM paradigm and ID paradigm of RSs, aiming to address the above challenges simultaneously. We first extend the vocabulary of pretrained LLMs with user/item ID tokens to faithfully model user/item collaborative and content semantics. Accordingly, a novel soft+hard prompting strategy is proposed to effectively learn user/item collaborative/content token embeddings via language modeling on RS-specific corpora, where each document is split into a prompt consisting of heterogeneous soft (user/item) tokens and hard (vocab) tokens and a main text consisting of homogeneous item tokens or vocab tokens to facilitate stable and effective language modeling. In addition, a novel mutual regularization strategy is introduced to encourage CLLM4Rec to capture recommendation-related information from noisy user/item content. Finally, we propose a novel recommendation-oriented finetuning strategy for CLLM4Rec, where an item prediction head with multinomial likelihood is added to the pretrained CLLM4Rec backbone to predict hold-out items based on soft+hard prompts established from masked user-item interaction history, where recommendations of multiple items can be generated efficiently without hallucination.
APT-Pipe: A Prompt-Tuning Tool for Social Data Annotation using ChatGPT
Authors: Yiming Zhu, Zhizhuo Yin, Gareth Tyson, Ehsan ul Haq, Lik-Hang Lee, Pan Hui
Venue: WWW
Year: 2024

Recent research has highlighted the potential of LLMs, like ChatGPT, for performing label annotation on social computing data. However, it is already well known that performance hinges on the quality of the input prompts. To address this, there has been a flurry of research into prompt tuning --- techniques and guidelines that attempt to improve the quality of prompts. Yet these largely rely on manual effort and prior knowledge of the dataset being annotated. To address this limitation, we propose APT-Pipe, an automated prompt-tuning pipeline. APT-Pipe aims to automatically tune prompts to enhance ChatGPT's text classification performance on any given dataset. We implement APT-Pipe and test it across twelve distinct text classification datasets. We find that prompts tuned by APT-Pipe help ChatGPT achieve higher weighted F1-score on nine out of twelve experimented datasets, with an improvement of 7.01% on average. We further highlight APT-Pipe's flexibility as a framework by showing how it can be extended to support additional tuning mechanisms.
Multimodal Pretraining and Generation for Recommendation: A Tutorial
Authors: Jieming Zhu, Xin Zhou, Chuhan Wu, Rui Zhang, Zhenhua Dong
Venue: WWW
Year: 2024

Personalized recommendation stands as a ubiquitous channel for users to explore information or items aligned with their interests. Nevertheless, prevailing recommendation models predominantly rely on unique IDs and categorical features for user-item matching. While this ID-centric approach has witnessed considerable success, it falls short in comprehensively grasping the essence of raw item contents across diverse modalities, such as text, image, audio, and video. This underutilization of multimodal data poses a limitation to recommender systems, particularly in the realm of multimedia services like news, music, and short-video platforms. The recent surge in pretraining and generation techniques presents both opportunities and challenges in the development of multimodal recommender systems. This tutorial seeks to provide a thorough exploration of the latest advancements and future trajectories in multimodal pretraining and generation techniques within the realm of recommender systems. The tutorial comprises four talks, addressing multimodal pretraining, multimodal fusion, multimodal generation, and presenting successful stories alongside open challenges in the field of recommendation. Our target audience encompasses scholars, practitioners, and other parties interested in this domain. By providing a succinct overview of the field, we aspire to facilitate a swift understanding of multimodal recommendation and foster meaningful discussions on the future development of this evolving landscape.
Robust Data-centric Graph Structure Learning for Text Classification
Authors: Jun Zhuang
Venue: WWW
Year: 2024

Over the past decades, text classification underwent remarkable evolution across diverse domains. Despite these advancements, most existing model-centric methods in text classification cannot generalize well on class-imbalanced datasets that contain high-similarity textual information. Instead of developing new model architectures, data-centric approaches enhance the performance by manipulating the data structure. In this study, we aim to investigate robust data-centric approaches that can help text classification in our collected dataset, the metadata of survey papers about Large Language Models (LLMs). In the experiments, we explore four paradigms and observe that leveraging arXiv's co-category information on graphs can help robustly classify the text data over the other three paradigms, conventional machine-learning algorithms, pre-trained language models' fine-tuning, and zero-shot / few-shot classifications using LLMs.
Knowledge Enhanced Multi-intent Transformer Network for Recommendation
Authors: Ding Zou, Wei Wei, Feida Zhu, Chuanyu Xu, Tao Zhang, Chengfu Huo
Venue: WWW
Year: 2024

Incorporating Knowledge Graphs (KGs) into Recommendation has attracted growing attention in industry, due to the great potential of KG in providing abundant supplementary information and interpretability for the underlying models. However, simply integrating KG into recommendation usually brings in negative feedback in industry, mainly due to the ignorance of the following two factors: i) users' multiple intents, which involve diverse nodes in KG. For example, in e-commerce scenarios, users may exhibit preferences for specific styles, brands, or colors. ii) knowledge noise, which is a prevalent issue in Knowledge Enhanced Recommendation (KGR) and even more severe in industry scenarios. The irrelevant knowledge properties of items may result in inferior model performance compared to approaches that do not incorporate knowledge. To tackle these challenges, we propose a novel approach named Knowledge Enhanced Multi-intent Transformer Network for Recommendation (KGTN), which comprises two primary modules: Global Intents Modeling with Graph Transformer, and Knowledge Contrastive Denoising under Intents. Specifically, Global Intents with Graph Transformer focuses on capturing learnable user intents, by incorporating global signals from user-item-relation-entity interactions with a well-designed graph transformer, and meanwhile learning intent-aware user/item representations. On the other hand, Knowledge Contrastive Denoising under Intents is dedicated to learning precise and robust representations. It leverages the intent-aware user/item representations to sample relevant knowledge, and subsequently proposes a local-global contrastive mechanism to enhance noise-irrelevant representation learning. Extensive experiments conducted on three benchmark datasets show the superior performance of our proposed method over the state-of-the-arts. And online A/B testing results on Alibaba large-scale industrial recommendation platform also indicate the real-scenario effectiveness of KGTN. The implementations are available at: https://github.com/CCIIPLab/KGTN.
AI for Science: The Next Big Opportunity
Authors: Jon Whittle
Venue: WWW
Year: 2025

In 2024, Demis Hassabis and John Jumper won the Nobel Prize for Chemistry for developing an AI model to predict the shape of every protein known to mankind. Chair of the Nobel Committee, Heiner Linke, described the AI system as the achievement of a ''50-year-old dream'' that solved a notoriously difficult problem eluding scientists since the 1970s. This AI system, AlphaFold, has ushered in a golden age of AI for science - the development of AI techniques for accelerating scientific discovery. We are currently amid the largest surge in the application and development of AI for scientific research in history. Scholarly publications, patents, education, training, research activity and investment are increasing at unprecedented rates. AI may help scientists address humanity's greatest challenges such as climate change, pollution, resource scarcity and infectious diseases. In this talk, I will cover the state of practice in AI for science. I'll discuss some of the successes already achieved as well as challenges for applying AI to science in the future. And I'll highlight some of the work at CSIRO, Australia's national science agency, which contains over 1000 AI experts developing novel AI solutions to long-standing scientific problems.
Distributed Ledger and Text Watermarking for Fine-Grain Provenance Checking of Textual Content
Authors: Flavio Bertini, Alessandro Benetton, Danilo Montesi
Venue: WWW
Year: 2025

Information disorder has become a major societal challenge, impacting public discourse and democracy. This phenomenon has been exacerbated by the spread of social media platforms, affecting various areas, ranging from national elections to public health. Addressing fake news through a manual approach (e.g., human fact-checking) is unfeasible due to the rapid production of textual content. At the same time, applying automatic tools is equally challenging, primarily due to the ambiguity of natural language. In this paper, we addressed online information disorder from a different perspective by proposing a platform that supports trustworthy and reputable news producers and enhances awareness among readers across various social media. Specifically, the proposed platform enables news producers to automatically embed a unique watermark in the text they create, ensuring that the news cannot be manipulated or misattributed. The watermarking is embedded in a fine-grained way, allowing even small extracts of the news to be shared while preserving traceability. Additionally, the association between the watermark and the news item is recorded in a distributed ledger, preventing further manipulation that could arise from centralised management. The aim is to enable readers to make more informed decisions about the content they encounter, even when engaging with excerpts of the original document, minimising reliance on external fact-checking organisations.
Policy-Guided Causal State Representation for Offline Reinforcement Learning Recommendation
Authors: Siyu Wang, Xiaocong Chen, Lina Yao
Venue: WWW
Year: 2025

In offline reinforcement learning-based recommender systems (RLRS), learning effective state representations is crucial for capturing user preferences that directly impact long-term rewards. However, raw state representations often contain high-dimensional, noisy information and components that are not causally relevant to the reward. Additionally, missing transitions in offline data make it challenging to accurately identify features that are most relevant to user satisfaction. To address these challenges, we propose Policy-Guided Causal Representation (PGCR), a novel two-stage framework for causal feature selection and state representation learning in offline RLRS. In the first stage, we learn a causal feature selection policy that generates modified states by isolating and retaining only the causally relevant components (CRCs) while altering irrelevant components. This policy is guided by a reward function based on the Wasserstein distance, which measures the causal effect of state components on the reward and encourages the preservation of CRCs that directly influence user interests. In the second stage, we train an encoder to learn compact state representations by minimizing the mean squared error (MSE) loss between the latent representations of the original and modified states, ensuring that the representations focus on CRCs. We provide a theoretical analysis proving the identifiability of causal effects from interventions, validating the ability of PGCR to isolate critical state components for decision-making. Extensive experiments demonstrate that PGCR significantly improves recommendation performance, confirming its effectiveness for offline RL-based recommender systems.