OpenRaiser/Intern-Atlas huggingface dataset:
On Practical Diversified Recommendation with Controllable Category Diversity Framework
Authors: Tao Zhang, Luwei Yang, Zhibo Xiao, Wen Jiang, Wei Ning
Venue: WWW
Year: 2024

Recommender systems have made significant strides in various industries, primarily driven by extensive efforts to enhance recommendation accuracy. However, this pursuit of accuracy has inadvertently given rise to echo chamber/filter bubble effects. Especially in industry, it could impair user's experiences and prevent user from accessing a wider range of items. One of the solutions is to take diversity into account. However, most of existing works focus on user's explicit preferences, while rarely exploring user's non-interaction preferences. These neglected non-interaction preferences are especially important for broadening user's interests in alleviating echo chamber/filter bubble effects. Therefore, in this paper, we first define diversity as two distinct definitions, i.e., user-explicit diversity (U-diversity) and user-item non-interaction diversity (N-diversity) based on user historical behaviors. Then, we propose a succinct and effective method, named as Controllable Category Diversity Framework (CCDF) to achieve both high U-diversity and N-diversity simultaneously. Specifically, CCDF consists of two stages, User-Category Matching and Constrained Item Matching. The User-Category Matching utilizes the DeepU2C model and a combined loss to capture user's preferences in categories, and then selects the top-K categories with a controllable parameter K. These top-K categories will be used as trigger information in Constrained Item Matching. Offline experimental results show that our proposed DeepU2C outperforms state-of-the-art diversity-oriented methods, especially on N-diversity task. The whole framework is validated in a real-world production environment by conducting online A/B testing. The improved conversion rate and diversity metrics demonstrate the superiority of our proposed framework in industrial applications. Further analysis supports the complementary effects between recommendation and search that diversified recommendation is able to effectively help users to discover new needs, and then inspire them to refine their demands in search.
Multi-round Counterfactual Generation: Interpreting and Improving Models of Text Classification
Authors: Huajie Zhang, Yuxin Ying, Fuzhen Zhuang, Haiqin Weng, Ying Sun, Zhao Zhang, Yiqi Tong, Yan Liu
Venue: WWW
Year: 2024

In recent years, natural language processing (NLP) models have demonstrated remarkable performance in text classification tasks. However, trust in the decision-making process requires a deeper understanding of the operational principles of these networks. Therefore, there is an urgent need to enhance transparency and the interpretability of these "black boxes". Aligned with this, we propose a model-agnostic interpretability method named MCG. This method generates counterfactual interpretations that are more faithful to the original models' performance through a multi-round dialogue, in which a new template is generated based on the evaluation of the previous counterfactual interpretation. In addition, MCG proposes a solution to improve model performance through counterfactual data augmentation for cases where the model to be interpreted is misclassified, which is rarely covered by existing counterfactual methods. Extensive experiments on three datasets demonstrate that our MCG outperforms current state-of-the-art methods in counterfactual generation for interpretability.
SymLearn: A Symbiotic Crowd-AI Collective Learning Framework to Web-based Healthcare Policy Adherence Assessment
Authors: Yang Zhang, Ruohan Zong, Lanyu Shang, Huimin Zeng, Zhenrui Yue, Dong Wang
Venue: WWW
Year: 2024

This paper develops a symbiotic human-AI collective learning framework that explores the complementary strengths of both AI and crowdsourced human intelligence to address a novel Web-based healthcare-policy-adherence assessment (WebHA) problem. In particular, the objective of the WebHA problem is to automatically assess people's public health policy adherence during emergent global health crisis events (e.g., COVID-19, MonkeyPox) by exploring massive social media imagery data. Recent advances in human-AI systems exhibit a significant potential in addressing the intricate imagery-based classification problems like WebHA by leveraging the collective intelligence of both humans and AI. This paper aims to address the limitation of existing human-AI systems that often rely heavily on human intelligence to improve AI model performance while overlooking the fact that humans themselves can be fallible and prone to errors. To address the above limitation, this paper develops SymLearn, a symbiotic human-AI co-learning framework that leverages human intelligence to troubleshoot and fine-tune the AI model while using AI models to guide human crowd workers to reduce the inherent human errors in their labels. Extensive experiments on two real-world WebHA applications show that SymLearn clearly outperforms the state-of-the-art baselines by improving WebHA performance and reducing crowd response delay.
User Response Modeling in Reinforcement Learning for Ads Allocation
Authors: Zhiyuan Zhang, Qichao Zhang, Xiaoxu Wu, Xiaowen Shi, Guogang Liao, Yongkang Wang, Xingxing Wang, Dongbin Zhao
Venue: WWW
Year: 2024

User response modeling can enhance the learning of user representations and further improve the reinforcement learning (RL) recommender agent. However, as users' behaviors are influenced by their long-term preferences and short-term stochastic factors (e.g., weather, mood, or fashion trends), it remains challenging for previous works focusing on recurrent neural network-based user response modeling. Meanwhile, due to the dynamic interests of users, it is often unrealistic to assume the dynamics of users are stationary. Drawing inspiration from opponent modeling, we propose a novel network structure, Deep User Q-Network (DUQN), incorporating a user response probabilistic model into the Q-learning ads allocation strategy to capture the effect of the non-stationary user policy on Q-values. Moreover, we utilize the Recurrent State-Space Model (RSSM) to develop the user response model, which includes deterministic and stochastic components, enabling us to fully consider user long-term preferences and short-term stochastic factors. In particular, we design a RetNet version of RSSM (R-RSSM) to support parallel computation. The R-RSSM model can be further used for multi-step predictions to enable bootstrapping over multiple steps simultaneously. Finally, we conduct extensive experiments on a large-scale offline dataset from the Meituan food delivery platform and a public benchmark. Experimental results show that our method yields superior performance to state-of-the-art (SOTA) baselines. Moreover, our model demonstrates a significant improvement in the online A/B test and has been fully deployed on the industrial Meituan platform, serving more than 500 million customers.
Efficient Computation of Signature-Restricted Views for Semantic Web Ontologies
Authors: Yizheng Zhao
Venue: WWW
Year: 2024

Uniform Interpolation (UI) is an advanced reasoning service used to narrow down an ontology to a restricted view. This new ontology, known as a uniform interpolant, will only consist of the ''relevant names'', yet it will retain their original meanings. UI is immensely promising due to its applicability across various domains where custom views of ontologies are essential. Nonetheless, to unlock its full potential, we need optimized techniques to generate these tailored views. Previous studies suggest that creating uniform interpolants for EL-ontologies is notably challenging. In some instances, it is not even feasible to compute a uniform interpolant; when feasible, the size of the uniform interpolant can be up to triple exponentially larger than the source ontology. Despite these challenges, our paper introduces an improved ''forgetting'' technique specifically designed for computing uniform interpolants of ELI-ontologies. We demonstrate that, with good normalization and inference strategies, such uniform interpolants can be efficiently computed, just as quickly as computing ''modules''. A comprehensive evaluation with a prototypical implementation of the method shows superb success rates over two popular benchmark datasets, demonstrating a clear computational advantage over state-of-the-art approaches.
Generating Multi-turn Clarification for Web Information Seeking
Authors: Ziliang Zhao, Zhicheng Dou
Venue: WWW
Year: 2024

Asking multi-turn clarifying questions has been applied in various conversational search systems to help recommend people, commodities, and images to users. However, its importance is still not emphasized in the Web search. In this paper, we make a step to extend the multi-turn clarification generation to Web search for clarifying users' ambiguous or faceted intents. Compared with other conversational search scenarios, Web search queries are more complicated, so clarification should be generated instead of being selected which is commonly applied in current studies. To this end, we first define the whole process of multi-turn Web search clarification composed of clarification candidate generation, optimal clarification selection, and document retrieval. Due to the lack of multi-turn open-domain clarification data, we first design a simple yet effective rule-based method to fit the above three components. After that, by utilizing the in-context learning and zero-shot instruction ability of large language models (LLMs), we implement clarification generation and selection by prompting LLMs with demonstrations and declarations, further improving the clarification effectiveness. To evaluate our proposed methods, we first measure whether our methods can improve the ability to retrieve documents. We also evaluate the quality of generated candidate facets. Experimental results show that, compared with existing single-turn methods for Web search clarification, our proposed framework is more suitable for open-domain Web search systems in asking multi-turn clarification questions to clarify users' ambiguous or faceted intents.
The MMO Economist: AI Empowers Robust, Healthy, and Sustainable P2W MMO Economies
Authors: Shiwei Zhao, Xi Yuan, Runze Wu, Zhipeng Hu, Haoyu Liu, Kai Wang, Yujing Hu, Tangjie Lv, Changjie Fan, Xin T. Tong, Jiangze Han, Yan Zheng, Jianye Hao
Venue: WWW
Year: 2024

Massively Multiplayer Online Games (MMOs) feature intricate virtual economies that permeate various in-game activities. However, the balancing act between profitability and equality in MMO economic design proves to be a persistent conundrum, especially in nascent business models like Pay-to-Win (P2W). Conventional efforts are curtailed by two primary constraints: the inability to verify and the provision of suboptimal solutions. In light of these predicaments, this paper delves into MMO economies and explores the promising potential of integrating emerging AI methodologies into economic design. Specifically, we introduce a novel hierarchical Reinforcement Learning (RL) solution for achieving Pareto optimality between profitability and equality in P2W economies. Leveraging our substantial industrial acumen and expertise, we establish an economic simulation environment that facilitates authentic and realistic assessments of MMO economic evolution. Building upon this foundation, we reconceptualize the P2W economic design process within the paradigm of a Markov Decision Process (MDP) and tackle it as a standard RL problem. Comprehensive evaluations corroborate that our solution demonstrates consistent personality specialization in economic simulations akin to real-world MMOs and significantly outperforms other baselines in economic design. Further discussions highlight its superiority in both frontier research and practical applications within the game industry.
Perennial Semantic Data Terms of Use for Decentralized Web
Authors: Rui Zhao, Jun Zhao
Venue: WWW
Year: 2024

In today's digital landscape, the Web has become increasingly centralized, raising concerns about user privacy violations. Decentralized Web architectures, such as Solid, offer a promising solution by empowering users with better control over their data in their personal 'Pods'. However, a significant challenge remains: users must navigate numerous applications to decide which application can be trusted with access to their data Pods. This often involves reading lengthy and complex Terms of Use agreements, a process that users often find daunting or simply ignore. This compromises user autonomy and impedes detection of data misuse. We propose a novel formal description of Data Terms of Use (DToU), along with a DToU reasoner. Users and applications specify their own parts of the DToU policy with local knowledge, covering permissions, requirements, prohibitions and obligations. Automated reasoning verifies compliance, and also derives policies for output data. This constitutes a "perennial'' DToU language, where the policy authoring only occurs once, and we can conduct ongoing automated checks across users, applications and activity cycles. Our solution is built on Turtle, Notation 3 and RDF Surfaces, for the language and the reasoning engine. It ensures seamless integration with other semantic tools for enhanced interoperability. We have successfully integrated this language into the Solid framework, and conducted performance benchmark. We believe this work demonstrates a practicality of a perennial DToU language and the potential of a paradigm shift to how users interact with data and applications in a decentralized Web, offering both improved privacy and usability.
Detecting Poisoning Attacks on Federated Learning Using Gradient-Weighted Class Activation Mapping
Authors: Jingjing Zheng, Kai Li, Xin Yuan, Wei Ni, Eduardo Tovar
Venue: WWW
Year: 2024

This paper proposes a new defense mechanism, namely, GCAMA, against model poisoning attacks on Federated learning (FL), which integrates Gradient-weighted Class Activation Mapping (GradCAM) and Autoencoder to offer a scientifically more powerful detection capability compared to existing Euclidean distance-based approaches. Particularly, GCAMA generates a heat map for each uploaded local model update, transforming each local model update into a lower-dimensional, visual representation, thereby accentuating the hidden features of the heat maps and increasing the success rate of identifying anomalous heat maps and malicious local models. We test ResNet-18 and MobileNetV3-Large deep learning models with CIFAR-10 and GTSRB datasets under Non-Independent and Identically Distributed (Non-IID) setting, respectively. The results demonstrate that GCAMA offers superior test accuracy of FL global model compared to the state-of-the-art methods. Our code is available at: https://github.com/jjzgeeks/GradCAM-AE
MULAN: Multi-modal Causal Structure Learning and Root Cause Analysis for Microservice Systems
Authors: Lecheng Zheng, Zhengzhang Chen, Jingrui He, Haifeng Chen
Venue: WWW
Year: 2024

Effective root cause analysis (RCA) is vital for swiftly restoring services, minimizing losses, and ensuring the smooth operation and management of complex systems. Previous data-driven RCA methods, particularly those employing causal discovery techniques, have primarily focused on constructing dependency or causal graphs for backtracking the root causes. However, these methods often fall short as they rely solely on data from a single modality, thereby resulting in suboptimal solutions. In this work, we propose Mulan, a unified multi-modal causal structure learning method designed to identify root causes in microservice systems. We leverage a log-tailored language model to facilitate log representation learning, converting log sequences into time-series data. To explore intricate relationships across different modalities, we propose a contrastive learning-based approach to extract modality-invariant and modality-specific representations within a shared latent space. Additionally, we introduce a novel key performance indicator-aware attention mechanism for assessing modality reliability and co-learning a final causal graph. Finally, we employ random walk with restart to simulate system fault propagation and identify potential root causes. Extensive experiments on three real-world datasets validate the effectiveness of our proposed method.
IDEA-DAC: Integrity-Driven Editing for Accountable Decentralized Anonymous Credentials via ZK-JSON
Authors: Shuhao Zheng, Zonglun Li, Junliang Luo, Ziyue Xin, Xue Liu
Venue: WWW
Year: 2024

Decentralized Anonymous Credential (DAC) systems are increasingly relevant, especially when enhancing revocation mechanisms in the face of complex traceability challenges. This paper introduces IDEA-DAC a paradigm shift from the conventional revoke-and-reissue methods, promoting direct and Integrity-Driven Editing (IDE) for Accountable DACs, which results in better integrity accountability, traceability, and system simplicity. We further incorporate an Edit-bound Conformity Check that ensures tailored integrity standards during credential amendments using R1CS-based ZK-SNARKs. Delving deeper, we propose ZK-JSON, a unique R1CS circuit design tailored for IDE over generic JSON documents. This design imposes strictly O(N) rank-1 constraints for variable-length JSON documents of up to N bytes in length, encompassing serialization, encryption, and edit-bound conformity checks. Additionally, our circuits only necessitate a one-time compilation, setup, and smart contract deployment for homogeneous JSON documents up to a specified size. While preserving core DAC features such as selective disclosure, anonymity, and predicate provability, IDEA-DAC achieves precise data modification checks without revealing private content, ensuring only authorized edits are permitted. In summary, IDEA-DAC offers an enhanced methodology for large-scale JSON-formatted credential systems, setting a new standard in decentralized identity management efficiency and precision.
Full Stage Learning to Rank: A Unified Framework for Multi-Stage Systems
Authors: Kai Zheng, Haijun Zhao, Rui Huang, Beichuan Zhang, Na Mou, Yanan Niu, Yang Song, Hongning Wang, Kun Gai
Venue: WWW
Year: 2024

The Probability Ranking Principle (PRP) has been considered as the foundational standard in the design of information retrieval (IR) systems. The principle requires an IR module's returned list of results to be ranked with respect to the underlying user interests, so as to maximize the results' utility. Nevertheless, we point out that it is inappropriate to indiscriminately apply PRP through every stage of a contemporary IR system. Such systems contain multiple stages (e.g., retrieval, pre-ranking, ranking, and re-ranking stages, as examined in this paper). The selection bias inherent in the model of each stage significantly influences the results that are ultimately presented to users. To address this issue, we propose an improved ranking principle for multi-stage systems, namely the Generalized Probability Ranking Principle (GPRP), to emphasize both the selection bias in each stage of the system pipeline as well as the underlying interest of users. We realize GPRP via a unified algorithmic framework named Full Stage Learning to Rank. Our core idea is to first estimate the selection bias in the subsequent stages and then learn a ranking model that best complies with the downstream modules' selection bias so as to deliver its top ranked results to the final ranked list in the system's output. We performed extensive experiment evaluations of our developed Full Stage Learning to Rank solution, using both simulations and online A/B tests in one of the leading short-video recommendation platforms. The algorithm is proved to be effective in both retrieval and ranking stages. Since deployed, the algorithm has brought consistent and significant performance gain to the platform.
NAT4AT: Using Non-Autoregressive Translation Makes Autoregressive Translation Faster and Better
Authors: Huanran Zheng, Wei Zhu, Xiaoling Wang
Venue: WWW
Year: 2024

With the increasing number of web documents, the demand for translation has increased dramatically. Non-autoregressive translation (NAT) models can significantly reduce decoding latency to meet the growing translation needs, but they sacrifice translation quality. And there is still an irreparable performance gap between NAT models and strong autoregressive translation (AT) models at the corpus level. However, more fine-grained comparative experiments on AT and NAT are currently lacking. Therefore, in this paper, we first conducted analysis experiments at the sentence level and found complementarity and high similarity between the translations generated by AT and NAT. Then, based on this observation, we propose a general and effective method called NAT4AT, which can not only use NAT to speed up the inference speed of AT significantly but also improve its final translation quality. Specifically, NAT4AT first uses a NAT model to generate an original translation in parallel and then uses an AT model as a correction model to revise errors in the original translation. In this way, the AT model no longer needs to predict the entire translation but only needs to predict a small number of error parts in the NAT result. Extensive experimental results on major WMT benchmarks verify the generality and effectiveness of our method, whose translation quality is superior to the strong AT model and achieves a 5.0x speedup.
On the Scale-Free Property of Citation Networks: An Empirical Study
Authors: Xiaoshi Zhong, Huizhi Liang
Venue: WWW
Year: 2024

Citation networks have been thought to exhibit scale-free property for many years; however, this assertion has been doubted recently. In this paper, we conduct extensive experiments to resolve this controversial issue. We firstly demonstrate the scale-free property in scale-free networks sampled from the popular Barabasi-Albert (BA) model. To this end, we employ a merged rank distribution, which is divided into outliers, power-law segment, and non-power-law data, to characterize network degrees, and propose a random sample consensus (RANSAC)-based method to identify power-law segments from merged rank distributions, and use the Kolmogorov-Smirnov (KS) test to examine the scale-free property in power-law segments. Subsequently, we apply the same methods to examine the scale-free property in real-world citation networks. Experimental results confirm the scale-free property in citation networks and attribute previous skepticism to the presence of outliers.
Poisoning Attack on Federated Knowledge Graph Embedding
Authors: Enyuan Zhou, Song Guo, Zhixiu Ma, Zicong Hong, Tao Guo, Peiran Dong
Venue: WWW
Year: 2024

Federated Knowledge Graph Embedding (FKGE) is an emerging collaborative learning technique for deriving expressive representations (i.e., embeddings) from client-maintained distributed knowledge graphs (KGs). However, poisoning attacks in FKGE, which lead to biased decisions by downstream applications, remain unexplored. This paper is the first work to systematize the risks of FKGE poisoning attacks, from which we develop a novel framework for poisoning attacks that force the victim client to predict specific false facts. Unlike centralized KGEs, FKGE maintains KGs locally, making direct injection of poisoned data challenging. Instead, attackers must create poisoned data without access to the victim's KG and inject it indirectly through FKGE aggregation. Specifically, to create poisoned data, the attacker first infers the targeted relations in the victim's local KG via a new KG component inference attack. Then, to accurately mislead the victim's embeddings via aggregation, the attacker locally trains a shadow model using the poisoned data and uses an optimized dynamic poisoning scheme to adjust the model and generate progressive poisoned updates. Our experimental results demonstrate the attack's effectiveness, achieving a remarkable success rate on various KGE models (e.g., 100% on TransE with WN18RR) while keeping the original task's performance nearly unchanged.