OpenRaiser/Intern-Atlas huggingface dataset:
Modeling User Viewing Flow using Large Language Models for Article Recommendation
Authors: Zhenghao Liu, Zulong Chen, Moufeng Zhang, Shaoyang Duan, Hong Wen, Liangyue Li, Nan Li, Yu Gu, Ge Yu
Venue: WWW
Year: 2024

This paper proposes the USer ViewING FLow ModEling (SINGLE) method for the article recommendation task, which models the user constant preference and instant interest from user-clicked articles. Specifically, we first employ a user constant viewing flow modeling method to summarize the user's general interest to recommend articles. In this case, we utilize Large Language Models (LLMs) to capture constant user preferences from previously clicked articles, such as skills and positions. Then we design the user instant viewing flow modeling method to build interactions between user-clicked article history and candidate articles. It attentively reads the representations of user-clicked articles and aims to learn the user's different interest views to match the candidate article. Our experimental results on the Alibaba Technology Association (ATA) website show the advantage of SINGLE, achieving a 2.4% improvement over previous baseline models in the online A/B test. Our further analyses illustrate that SINGLE has the ability to build a more tailored recommendation system by mimicking different article viewing behaviors of users and recommending more appropriate and diverse articles to match user interests.
A Non-Intrusive Approach to Assessing Dysarthria Severity: Advancing Clinical Diagnosis
Authors: Ganjun Liu, Xiaohui Hou, Meng Ge, Tao Zhang, Haizhou Li
Venue: WWW
Year: 2024

AI-driven severity assessment techniques for dysarthric disorders show promise in aiding speech-language pathologists with diagnostics and therapeutic follow-ups for patients. Existing solutions generally focus on the average intelligibility and hoarseness of the individual speaker's speech (i.e., speaker-level classification). This potentially ignores the slight variations in pronunciation attributed to the speaker's dysarthric disorders, e.g., /t/ and /d/. To address this issue, we rethink the inherent differences in the dysarthria speech, and propose a non-intrusive severity assessment approach called DysarNet. Specifically, we first design a prosodic emphasis module based on frame-level speech features to highlight the fine-grained temporal changes including pronunciation content, rhythm, and timing. Second, we design a multi-scale aggregation strategy to collect statistical cues on articulatory information at different scales, i.e., frame-level and utterance-level. By doing so, multi-scale prosody and articulatory cues are directly assist the prediction network for assessing dysarthria severity from multiple views, and naturally achieve speaker-independent generalization ability. Experimental results on VCC 2018 and TORGO datasets show that our DysarNet excels in assessing dysarthria severity.
LLaCE: Locally Linear Contrastive Embedding
Authors: Ruichen Liu, Yang Liu, Jiming Liu
Venue: WWW
Year: 2024

Node embedding is one of the most widely adopted techniques in numerous graph analysis tasks, such as node classification. Methods for node embedding can be broadly classified into three categories: proximity matrix factorization approaches, sampling methods, and deep learning strategies. Among the deep learning strategies, graph contrastive learning has attracted significant interest. Yet, it has been observed that existing graph contrastive learning approaches do not adequately preserve the local topological structure of the original graphs, particularly when neighboring nodes belong to disparate categories. To address this challenge, this paper introduces a novel node embedding approach named Locally Linear Contrastive Embedding (LLaCE). LLaCE is designed to maintain the intrinsic geometric structure of graph data by utilizing locally linear formulation, thereby ensuring that the local topological characteristics are accurately reflected in the embedding space. Experimental results on one synthetic dataset and five real-world datasets validate the effectiveness of our proposed method.
From Promises to Practice: Evaluating the Private Browsing Modes of Android Browser Apps
Authors: Xiaoyin Liu, Wenzhi Li, Qinsheng Hou, Shishuai Yang, Lingyun Ying, Wenrui Diao, Yanan Li, Shanqing Guo, Haixin Duan
Venue: WWW
Year: 2024

Private browsing is a common feature of web browsers on desktop platforms. This feature protects the privacy of users browsing the Internet and, therefore, is widely welcomed by users. In recent years, with the popularity of smartphones, the private browsing mode has been introduced into mobile browsers. However, its deployment on mobile platforms has not been well evaluated. To bridge the gap, in this work, we systemically studied the private browsing modes of Android browser apps. Specifically, we proposed six private rules for mobile browsers to follow by combining the mobile browsing features with the previous research on private browsing. Furthermore, we designed an automated analysis framework, BroDroid, to detect whether mobile browsers violate these rules. Also, with BroDroid, we evaluated 49 popular browser apps crawled from Google Play. Finally, BroDroid successfully identified 58 violations, some of which come from the promised capabilities of the browser. We reported our discovered issues to the corresponding developers, and four of them (Yandex Browser, Mint Browser, Web Explorer, and Net Fast Web Browser) have acknowledged our findings. Our observation may be the tip of the iceberg, and more efforts should be put into improving the privacy protections of mobile browsers.
GraphSAGE-based POI Recommendation via Continuous-Time Modeling
Authors: Yuwen Liu, Lianyong Qi, Weiming Liu, Xiaolong Xu, Xuyun Zhang, Wanchun Dou
Venue: WWW
Year: 2024

With the proliferation of Location-based Social Networks (LBSNs), user check-in data at Points-of-Interest (POIs) has surged, reshaping user-environment interaction. However, POI recommendation remains a challenging task for two primary reasons. First, external incentives often drive users' check-ins, potentially misrepresenting their genuine preferences. Second, while many current research model the temporal dynamics of user preferences in a discrete space, they ignore capturing the continuous evolution of these preferences. To address these challenges, we propose the GraphSAGE-based POI Recommendation via Continuous-Time Modeling (GSA-CTM). We first utilize GraphSAGE to identify real user preferences and filter out noise beyond the user's real preferences. After GraphSAGE captures complex interaction, we use Gated Recurrent Unit (GRU) combined with neural Ordinary Differential Equations (ODEs) to capture the temporal information embedded in the interaction, and then use neural ODEs to model the user's continuous dynamic preferences into continuous space. Experiments on two widely-used public datasets validate the superiority of our method.
Inductive Cognitive Diagnosis for Fast Student Learning in Web-Based Intelligent Education Systems
Authors: Shuo Liu, Junhao Shen, Hong Qian, Aimin Zhou
Venue: WWW
Year: 2024

Cognitive diagnosis aims to gauge students' mastery levels based on their response logs. Serving as a pivotal module in web-based online intelligent education systems (WOIESs), it plays an upstream and fundamental role in downstream tasks like learning item recommendation and computerized adaptive testing. WOIESs are open learning environment where numerous new students constantly register and complete exercises. In WOIESs, efficient cognitive diagnosis is crucial to fast feedback and accelerating student learning. However, the existing cognitive diagnosis methods always employ intrinsically transductive student-specific embeddings, which become slow and costly due to retraining when dealing with new students who are unseen during training. To this end, this paper proposes an inductive cognitive diagnosis model (ICDM) for fast new students' mastery levels inference in WOIESs. Specifically, in ICDM, we propose a novel student-centered graph (SCG). Rather than inferring mastery levels through updating student-specific embedding, we derive the inductive mastery levels as the aggregated outcomes of students' neighbors in SCG. Namely, SCG enables to shift the task from finding the most suitable student-specific embedding that fits the response logs to finding the most suitable representations for different node types in SCG, and the latter is more efficient since it no longer requires retraining. To obtain this representation, ICDM consists of a construction-aggregation-generation-transformation process to learn the final representation of students, exercises and concepts. Extensive experiments across real-world datasets show that, compared with the existing cognitive diagnosis methods that are always transductive, ICDM is much more faster while maintains the competitive inference performance for new students.
Understanding Deployment Experience of 5G
Authors: Ziyi Liu, Huandong Wang, Yong Li
Venue: WWW
Year: 2024

The global rollout of 5G mobile networks has prompted discussions on deployment strategies. Given the knowledge gap in the current deployment strategies of 5G base stations, understanding the deployment experience from regions with widespread 5G base stations is valuable for guiding future deployments elsewhere. In this study, based on a large data set collected from a metropolitan city in China, we discover the misalignment between 5G traffic demand and the number of base stations. Then we introduce a factor to quantify the misalignment. Our analysis indicates the following important observations. Firstly, unique traffic patterns of functional areas contribute to different misalignment factors, i.e., transport areas exhibit a positive factor, in contrast to the negative factor observed in urban comprehensive and residential areas. Secondly, regions with a high density of base stations still suffer from low energy and resource utilization efficiency due to their high energy consumption. Thirdly, our analysis reveals that 5G base stations are frequently located in areas with large 4G traffic, yet the incomplete migration of traffic to 5G results in misalignment. This understanding of the 5G deployment experience can help further studies on optimizing energy efficiency and network utilization rate of the mobile networks.
De-Anchor: Mitigating Attention Polarization for Lifelong User Behavior Modeling in Click-Through Rate Prediction
Authors: Hongzun Liu, Kang Yin, Tianyu Sun, Rui Huang, Yunsong Li, Xiao Fang, Zhaojie Liu, Weidong Liu, Guorui Zhou
Venue: WWW
Year: 2024

User lifelong behavior sequences are essential for click-through rate (CTR) prediction tasks in industrial recommender systems. Attention-based module, especially multi-head target attention (MHTA), has been proven to be effective in aggregating behavior features given a certain target item. However, we found a common phenomenon that attention weights in MHTA tend to over-concentrate on merely a small subset of a user's historical behaviors, producing a sparse one-hot distributed attention weights in training gradually, which we callAttention Polarization (AP). These polarized weights on certain behaviors (which we call "\textitattention anchor ") could make the model fail to capture a user's diversified interests, and harm the learning of behavior embedding as the gradients on these features are nearly zero. We introduce two indicators:anchor rate andattention entropy to measure the magnitude of AP, and proposeDe-Anchor , a novel method to alleviate it, which can serve as a stand-alone and parameter-efficient plug-in to existing CTR backbones. De-Anchor contains two modules:Anchor-aware gradient dropout (AGD) forcing the model to capture diversified interest information from behavior sequences by discarding gradients of non-behavior features, andTarget-aware attention anchor (TAA) providing a pseudo behavior to offload excessive weights of MHTA. Extensive offline experiments and industrial online A/B tests demonstrate the efficacy of our method.
Mining Exploratory Queries for Conversational Search
Authors: Wenhan Liu, Ziliang Zhao, Yutao Zhu, Zhicheng Dou
Venue: WWW
Year: 2024

Users' queries are usually vague, and their search intents tend to be ambiguous, thereby needing search clarification to clarify users' current intent by asking a clarifying question and providing several clickable sub-intent items as clarification options. However, in addition to drilling down the current query, users may also have exploratory needs that diverge from their current intent. For example, a user searching for the query "Cartier women watches'' may also potentially want to explore some parallel information by issuing queries such as "Rolex women watches'' or "Cartier women bracelets'', named exploratory queries in this paper. These exploratory needs are common during the search process yet cannot be satisfied by current search clarification approaches which typically stick to the sub-intents of the query. This paper focuses on mining exploratory queries as additional options to meet users' exploratory needs in conversational search systems. Specifically, we first design a rule-based model that generates exploratory queries based on the current query's top retrieved documents. Then, we propose using the data generated by the rule-based model to train a neural generation model through multi-task learning for further generalization. Finally, we borrow the in-context learning ability of the large language model to generate exploratory queries based on prompt engineering. We constructed an evaluation dataset based on human annotations and conduct an extensive set of experiments. The results show that our proposed methods generate higher-quality exploratory queries compared with several baselines.
Author Name Disambiguation via Paper Association Refinement and Compositional Contrastive Embedding
Authors: Dezhi Liu, Richong Zhang, Junfan Chen, Xinyue Chen
Venue: WWW
Year: 2024

Author name disambiguation (AND) is an essential task for online academic retrieval systems. Recent models adopt representation learning in the author's name disambiguation. Despite achieving remarkable success, these methods may be limited in two aspects. First, the heuristically constructed paper association graphs used for representation learning contain uncertainties that may cause negative supervision. Second, existing algorithms, such as binary cross-entropy loss, used to train representation learning models may not produce sufficiently high-quality representations for AND. To tackle the above problems, we propose an association refining and compositional contrasting (ARCC) framework for AND tasks. ARCC first adopts an iterative graph structure refinement process to dynamically reduce the uncertainties in paper graphs. Then, a compositional contrastive learning method is proposed to encourage learning more discriminative representations for AND. Empirical studies on two benchmark datasets suggest that ARCC is effective for AND and outperforms the state-of-the-art models.
Benchmarking News Recommendation in the Era of Green AI
Authors: Qijiong Liu, Jieming Zhu, Quanyu Dai, Xiao-Ming Wu
Venue: WWW
Year: 2024

Over recent years, news recommender systems have gained significant attention in both academia and industry, emphasizing the need for a standardized benchmark to evaluate and compare the performance of these systems. Concurrently, Green AI advocates for reducing the energy consumption and environmental impact of machine learning. To address these concerns, we introduce the first Green AI benchmarking framework for news recommendation, known as GreenRec, and propose a metric for assessing the tradeoff between recommendation accuracy and efficiency. Our benchmark encompasses 30 base models and their variants, covering traditional end-to-end training paradigms as well as our proposed efficient only-encode-once (OLEO) paradigm. Through experiments consuming 2000 GPU hours, we observe that the OLEO paradigm achieves competitive accuracy compared to state-of-the-art end-to-end paradigms and delivers up to a 2992% improvement in sustainability metrics.
InArt: In-Network Aggregation with Route Selection for Accelerating Distributed Training
Authors: Jiawei Liu, Yutong Zhai, Gongming Zhao, Hongli Xu, Jin Fang, Zhen Zeng, Ying Zhu
Venue: WWW
Year: 2024

Deep learning has brought about a revolutionary transformation in network applications, particularly in domains like e-commerce and online advertising. Distributed training (DT), as a critical means to expedite model training, has progressively emerged as a key foundational infrastructure for such applications. However, with the rapid advancement of hardware accelerators, the performance bottleneck in DT has shifted from computation to communication. In-network aggregation (INA) solutions have shown promise in alleviating the communication bottleneck. Regrettably, current INA solutions primarily focus on improving efficiency under the traditional parameter server (PS) architecture and do not fully address the communication bottleneck caused by limited PS ingress bandwidth. To bridge this gap, we propose InArt, the first work to introduce INA with routing selection in a multi-PS architecture. InArt employs a multi-PS architecture to split DT tasks among multiple PSs, and selects appropriate routing schemes to fully harness INA capabilities. To accommodate traffic dynamics, InArt adopts a two-phase approach: splitting the training model among multiple parameter servers and selecting routing paths for INA. We propose Lagrange multiplier and randomized rounding algorithms for these phases, respectively. We implement InArt and evaluate its performance through experiments on physical platforms (Tofino switches) and Mininet emulation (P4 Software Switches). Experimental results show that InArt can reduce communication time by 48%\!\sim57\!% compared with state-of-the-art solutions.
Burstiness-aware Bipartite Graph Neural Networks for Fraudulent User Detection on Rating Platforms
Authors: Yen-Wen Lu, Yu-Che Tsai, Cheng-Te Li
Venue: WWW
Year: 2024

As the digital commerce landscape continues to expand with rating platforms, the consumer base has similarly grown, marking a pivotal reliance on user ratings and reviews. However, the rise of fraudulent users leveraging deceitful conduct, such as manipulation of product rankings, challenges the credibility of these platforms and compels the necessity for an effective detection model. Amid the challenges of evolving fraudulent patterns and label scarcity, this paper presents a novel model, Burstiness-aware Bipartite Graph Neural Networks (BurstBGN), which combats fraud by exploiting user-product bipartite graphs and timestamped rating activities. BurstBGN encapsulates two key ideas: the modeling of user-product interaction via historical rating data through an Edge-time GNN module and the exhaustive mapping of bursty fraudulent user activities. The performance of BurstBGN is demonstrated through rigorous benchmarking against established methods across three datasets. Our results show that BurstBGN consistently outperforms these methods under both transductive and inductive settings, confirming its effectiveness in detecting fraudulent users from limited annotated data, and thereby providing a safeguard for maintaining user trust in e-commerce platforms.
Automating the Information Extraction from Semi-Structured Interview Transcripts
Authors: Angelina Parfenova Lucerne
Venue: WWW
Year: 2024

This paper explores the development and application of an automated system designed to extract information from semi-structured interview transcripts. Given the labor-intensive nature of traditional qualitative analysis methods, such as coding, there exists a significant demand for tools that can facilitate the analysis process. Our research investigates various topic modeling techniques and concludes that the best model for analyzing interview texts is a combination of BERT embeddings and HDBSCAN clustering. We present a user-friendly software prototype that enables researchers, including those without programming skills, to efficiently process and visualize the thematic structure of interview data. This tool not only facilitates the initial stages of qualitative analysis but also offers insights into the interconnectedness of topics revealed, thereby enhancing the depth of qualitative analysis.
When Crypto Economics Meet Graph Analytics and Learning
Authors: Bingqiao Luo
Venue: WWW
Year: 2024

Utilizing graph analytics and learning has proven to be an effective method for exploring aspects of crypto economics such as network effects, decentralization, tokenomics, and fraud detection. However, the majority of existing research predominantly focuses on leading cryptocurrencies, namely Bitcoin (BTC) and Ethereum (ETH), overlooking the vast diversity among the more than 10,000 cryptocurrency projects. This oversight may result in skewed insights. In our paper, we aim to broaden the scope of investigation to encompass the entire spectrum of cryptocurrencies, examining various coins across their entire life cycles. Furthermore, we intend to pioneer advanced methodologies, including graph transfer learning and the innovative concept of "graph of graphs". By extending our research beyond the confines of BTC and ETH, our goal is to enhance the depth of our understanding of crypto economics and to advance the development of more intricate graph-based techniques.
Message Injection Attack on Rumor Detection under the Black-Box Evasion Setting Using Large Language Model
Authors: Yifeng Luo, Yupeng Li, Dacheng Wen, Liang Lan
Venue: WWW
Year: 2024

Recent analyses have disclosed that existing rumor detection techniques, despite playing a pivotal role in countering the dissemination of misinformation on social media, are vulnerable to both white-box and surrogate-based black-box adversarial attacks. However, such attacks depend heavily on unrealistic assumptions, e.g., modifiable user data and white-box access to the rumor detection models, or appropriate selections of surrogate models, which are impractical in the real world. Thus, existing analyses fail to uncover the robustness of rumor detectors in practice. In this work, we take a further step towards the investigation about the robustness of existing rumor detection solutions. Specifically, we focus on the state-of-the-art rumor detectors, which leverage graph neural network based models to predict whether a post is rumor based on the Message Propagation Tree (MPT), a conversation tree with the post as its root and the replies to the post as the descendants of the root. We propose a novel black-box attack method, HMIA-LLM, against these rumor detectors, which uses the Large Language Model to generate malicious messages and inject them into the targeted MPTs. Our extensive evaluation conducted across three rumor detection datasets, four target rumor detectors, and three baselines for comparison demonstrates the effectiveness of our proposed attack method in compromising the performance of the state-of-the-art rumor detectors.