OpenRaiser/Intern-Atlas huggingface dataset:
Recall-Augmented Ranking: Enhancing Click-Through Rate Prediction Accuracy with Cross-Stage Data
Authors: Junjie Huang, Guohao Cai, Jieming Zhu, Zhenhua Dong, Ruiming Tang, Weinan Zhang, Yong Yu
Venue: WWW
Year: 2024

Click-through rate (CTR) prediction plays an indispensable role in online recommendation and advertising platforms. Numerous deep learning based models have been proposed to improve CTR prediction accuracy, and they typically leverage user behavior sequences to capture users' shifting preferences. However, these historical sequences of user interactions often suffer from severe homogeneity and scarcity compared to the extensive item pool. Relying solely on such sequences for user representations is inherently restrictive, as user interests extend beyond the scope of items they have previously engaged with. To address this challenge, we propose a data-driven approach to enrich user representations.We recognize user profiling and recall items as two ideal data sources within the cross-stage framework, encompassing the u2u (user-to-user) and i2i (item-to-item) aspects, respectively, because of their higher relevance to target users and ranking items, as well as their greater diversity. In this paper, we propose a novel architecture named Recall-Augmented Ranking (RAR).RAR consists of two key sub-modules, namely the Cross-Stage User and Item Selection Module and the Co-Interaction Module. These sub-modules synergistically gather information from a vast pool of look-alike users and recall items, resulting in enriched user representations. Notably, RAR is orthogonal to many existing CTR models, allowing for seamless integration and consistent performance improvements in a plug-and-play manner. Extensive experiments are conducted on CTR prediction benchmarks, which verify the efficacy and compatibility of RAR against state-of-the-art methods.
Advancing Web 3.0: Making Smart Contracts Smarter on Blockchain
Authors: Junqin Huang, Linghe Kong, Guanjie Cheng, Qiao Xiang, Guihai Chen, Gang Huang, Xue Liu
Venue: WWW
Year: 2024

Blockchain and smart contracts are one of the key technologies promoting Web 3.0. However, due to security considerations and consistency requirements, smart contracts currently only support simple and deterministic programs, which significantly hinders their deployment in intelligent Web 3.0 applications. To enhance smart contracts intelligence on the blockchain, we propose SMART, a plug-in smart contract framework that supports efficient AI model inference while being compatible with existing blockchains. To handle the high complexity of model inference, we propose an on-chain and off-chain joint execution model, which separates the SMART contract into two parts: the deterministic code still runs inside an on-chain virtual machine, while the complex model inference is offloaded to off-chain compute nodes. To solve the non-determinism brought by model inference, we leverage Trusted Execution Environments (TEEs) to endorse the integrity and correctness of the off-chain execution. We also design distributed attestation and secret key provisioning schemes to further enhance the system security and model privacy. We implement a SMART prototype and evaluate it on a popular Ethereum Virtual Machine (EVM)-based blockchain. Theoretical analysis and prototype evaluation show that SMART not only achieves the security goals of correctness, liveness, and model privacy, but also has approximately 5 orders of magnitude faster inference efficiency than existing on-chain solutions.
Cost-effective Data Labelling for Graph Neural Networks
Authors: Shixun Huang, Ge Lee, Zhifeng Bao, Shirui Pan
Venue: WWW
Year: 2024

Active learning (AL), that aims to label limited data samples to effectively train the model, stands as a very cost-effective data labelling strategy in machine learning. Given the state-of-the-art performance GNNs have achieved in graph-based tasks, it is critical to design proper AL methods for graph neural networks (GNNs). However, existing GNN-based AL methods require considerable supervised information to guide the AL process, such as the GNN model to use, and initially labelled nodes and labels of newly selected nodes. Such dependency on supervised information limits both flexibility and scalabilty. In this paper, we propose an unsupervised, scalable and flexible AL method - it incurs low memory footprints and time cost, is flexible to the choice of underlying GNNs, and operates without requiring GNN-model-specific knowledge or labels of selected nodes. Specifically, we leverage the commonality of existing GNNs to reformulate the unsupervised AL problem as the Aggregation Involvement Maximization (AIM) problem. The objective of AIM is to maximize the involvement or participation of all nodes during the feature aggregation process of GNNs for nodes to be labelled. In this way, the aggregated features of labelled nodes can be diversified to a large extent, thereby benefiting the training of feature transformation matrices which are major trainable components in GNNs. We prove that the AIM problem is NP-hard and propose an efficient solution with theoretical guarantees. Extensive experiments on public datasets demonstrate the effectiveness, scalability and flexibility of our method. Our study is highly relevant to the track "Graph Algorithms and Modeling for the Web" since we focus one of the major listed topics "Graph Embedding and GNNs for the Web" and AL for GNNs, as an important research problem, is faced by aforementioned challenges to be tackled in this paper.
From Creation to Clarification: ChatGPT's Journey Through the Fake News Quagmire
Authors: Yue Huang, Kai Shu, Philip S. Yu, Lichao Sun
Venue: WWW
Year: 2024

The rampant spread of fake news has adversely affected society, resulting in extensive research on curbing its spread. As a notable milestone in large language models (LLMs), ChatGPT has gained significant attention due to its exceptional capabilities. In this study, we present an exploration of ChatGPT's proficiency in generating, explaining, and detecting fake news as follows.Generation -- We employ different prompt methods to generate fake news and prove the high quality of these instances through both self-assessment and human evaluation.Explanation -- We obtain nine features to characterize fake news based on ChatGPT's explanations and analyze the distribution of these factors across multiple public datasets.Detection -- We examine ChatGPT's capacity to identify fake news. We propose a reason-aware prompt method to improve its performance. We further probe into the potential extra information that could bolster its effectiveness in detecting fake news.
Entire Chain Uplift Modeling with Context-Enhanced Learning for Intelligent Marketing
Authors: Yinqiu Huang, Shuli Wang, Min Gao, Xue Wei, Changhao Li, Chuan Luo, Yinhua Zhu, Xiong Xiao, Yi Luo
Venue: WWW
Year: 2024

Uplift modeling, vital in online marketing, seeks to accurately measure the impact of various strategies, such as coupons or discounts, on different users by predicting the Individual Treatment Effect (ITE). In an e-commerce setting, user behavior follows a defined sequential chain, including impression, click, and conversion. Marketing strategies exert varied uplift effects at each stage within this chain, impacting metrics like click-through and conversion rate. Despite its utility, existing research has neglected to consider the inter-task across all stages impacts within a specific treatment and has insufficiently utilized the treatment information, potentially introducing substantial bias into subsequent marketing decisions. We identify these two issues as the chain-bias problem and the treatment-unadaptive problem. This paper introduces the Entire Chain UPlift method with context-enhanced learning (ECUP), devised to tackle these issues. ECUP consists of two primary components: 1) the Entire Chain-Enhanced Network, which utilizes user behavior patterns to estimate ITE throughout the entire chain space, models the various impacts of treatments on each task, and integrates task prior information to enhance context awareness across all stages, capturing the impact of treatment on different tasks, and 2) the Treatment-Enhanced Network, which facilitates fine-grained treatment modeling through bit-level feature interactions, thereby enabling adaptive feature adjustment. Extensive experiments on public and industrial datasets validate ECUP's effectiveness. Moreover, ECUP has been deployed on the Meituan food delivery platform, serving millions of daily active users, with the related dataset released for future research.
Data Quality-based Gradient Optimization for Recurrent Neural Networks
Authors: Feihu Huang, Peiyu Yi, Shan Li, Haiwen Xu
Venue: WWW
Year: 2024

Time series forecasting holds significant value in various application scenarios. However, existing forecasting methods primarily focus on optimizing model architecture while neglecting the substantial impact of data quality on model learning. In this study, we aim to enhance model performance by optimizing data utilization based on data quality and propose a Data Quality-based Gradient Optimization (DQGO) method to facilitate training of recurrent neural networks. Firstly, we define sample quality as the matching degree between samples and model, and suggest using the attention entropy to calculate the sample quality through an attention mechanism. Secondly, we optimize the model's gradient vector by giving different weights to samples with different quality. Through experiments conducted on six datasets, the results demonstrate that DQGO significantly improves LSTM's performance. In certain cases, it even surpasses the state-of-the-art models.
Seamlessly Transferring Assets through Layer-0 Bridges: An Empirical Analysis of Stargate Bridge's Architecture and Dynamics
Authors: Chuanshan Huang, Tao Yan, Claudio J. Tessone
Venue: WWW
Year: 2024

The increasing number of distinct blockchains has led to a growing need for data exchange and asset transfer across various isolated blockchains. To address this, cross-chain bridges have emerged as a critical mechanism for enabling interoperability and facilitating data and asset exchange across diverse blockchains. Among these bridges, the Layer-0 bridge stands out as a scalability solution that enhances blockchain performance at the foundational layer of data transition, without altering the blockchain's structure. Stargate is a notable Layer-0 Lock-and-Unlock cross-chain bridge that supports transactions across various EVM-based blockchains, with the highest Total Value Locked (TVL) among cross-chain bridges of the same kind. While previous cross-chain research has primarily focused on Layer-2 bridges, this study specifically examines Stargate and analyzes its dynamics as well as potential vulnerabilities. We collect transaction data of Stargate on six blockchains including Ethereum, Polygon, Binance Smart Chain, Avalanche, Arbitrum and Optimism. Our findings reveal the transaction patterns and evidence of exploitations of Stargate by investigating its transaction dynamics over time.
Automatic Design Summary Generation with Generative AI
Authors: Daisuke Ikoma, Eisuke Aoki, Tomoki Taniguchi, Shinya Suzuki, Tomoko Ohkuma
Venue: WWW
Year: 2024

In the private residential sales market, obtaining orders for exterior design requires a proposal considering the constraints of the location and customer. The design summary is a proposal document that describes concepts at the earliest stage of exterior design. It not only appeals to the customer, but also is referenced in the design drawing. However, the quality varies depending on the skill of the creator because the construction of a design summary requires the knowledge of human experts. This paper aims to generate the design summary using generative AI. Firstly, we analyze the characteristics of the design summary to identify the essential elements. Then, we propose a sequence of prompts to generate the design summary. Finally, we conducted a comparative evaluation between design summaries created by experts and those generated by generative AI.
Position Bias Estimation with Item Embedding for Sparse Dataset
Authors: Shion Ishikawa, Yun Ching Liu, Young-joo Chung, Yu Hirate
Venue: WWW
Year: 2024

Estimating position bias is a well-known challenge in Learning to Rank (L2R). Click data in e-commerce applications, such as targeted advertisements and search engines, provides implicit but abundant feedback to improve personalized rankings. However, click data inherently includes various biases like position bias. Based on the position-based click model, Result Randomization and Regression Expectation-Maximization algorithm (REM) have been proposed to estimate position bias, but they require various paired observations of (item, position). In real-world scenarios of advertising, marketers frequently display advertisements in a fixed pre-determined order, which creates difficulties in estimation due to the limited availability of various pairs in the training data, resulting in a sparse dataset. We propose a variant of the REM that utilizes item embeddings to alleviate the sparsity of (item, position). Using a public dataset and internal carousel advertisement click dataset, we empirically show that item embedding with Latent Semantic Indexing (LSI) and Variational Auto-Encoder (VAE) improves the accuracy of position bias estimation and the estimated position bias enhances Learning to Rank performance. We also show that LSI is more effective as an embedding creation method for position bias estimation.
Tender Document Analyzer with the Combination of Supervised Learning and LLM-based Improver
Authors: Tomoki Ito, Shun Nakagawa
Venue: WWW
Year: 2024

Bidders often take a long time to read and understand tender documents because they require specialized knowledge, and tender documents are generally long. Bidders first overview the specific items, such as payment and warranty, in a tender document and then check the overall document. Therefore, the function that can extract specific items (i.e., item extractor) and the function that can highlight words or phrases related to specific items (i.e., word-phrase highlighter) are in great demand. To develop the above two types of functions, we need to solve two problems. The first problem is the problem related to the annotated data set. The second problem concerns the BERT NER-based prediction approach in a small training dataset setting. To solve the first problem, we created two types of sequence labeling datasets related to Item Extractor and Word-Phrase Highlighter. To solve the second problem, we propose the Information Extraction (IE) method, which combines (1) a supervised learning approach using Bidirectional Encoder Representations from Transformers (BERT) and (2) a large language model (LLM)-based improver. We then developed the web application system called Tender Document Analyzer (TDDA), which includes "Item Extractor" and "Word-Phrase Highlighter". Experimental evaluation shows that our approach is practical. Firstly, the evaluation for extraction ability shows that the performance of our proposed method is much higher than the baseline approach that uses GPT 3.5, as well as demonstrates that the proposed LLM-based improver can improve the IE ability. In addition, the usability evaluation shows that bidders can solve the task in less time using our system.
Investigations of Top-Level Domain Name Collisions in Blockchain Naming Services
Authors: Daiki Ito, Yuta Takata, Hiroshi Kumagai, Masaki Kamizono
Venue: WWW
Year: 2024

Traditionally, top-level domains (TLDs) are managed by the Internet corporation for assigned names and numbers (ICANN), and the domain names under them are managed by registrars. Against such a centralized management, a blockchain naming service (BNS) has been proposed to manage TLDs on blockchains without authority intervention. BNS users can register TLD strings as non-fungible tokens and manage the TLD root zone. However, such a decentralized management results in the introduction of a new security issue, BNS TLD name collision, wherein the same TLD is registered in several different BNSs. In this study, we investigated BNS TLD name collisions by analyzing TLDs registered on two BNSs: Handshake and Decentraweb. Specifically, we collected TLDs registered in Handshake and Decentraweb and the associated data, and analyzed the data registration status of BNS TLDs and BNS TLD name collisions. The analysis of 11,595,406 Handshake and 11,889 Decentraweb TLDs revealed 6,973 BNS TLD name collisions. In particular, lastname TLDs, which are intended for use as person names, yielded a large number of registered domain names. In addition, the analysis identified 10 name collisions between the BNS and operational ICANN TLDs. Further, the ICANN TLD candidates under review also had name collisions against the BNS TLDs. Consequently, based on the characteristics of these name collisions and discussions in BNS communities, we considered countermeasures against BNS TLD name collisions. For the further development of BNSs, we believe that it is essential to discuss with the existing Internet communities and coexist with the existing Internet.
Dual Box Embeddings for the Description Logic EL++
Authors: Mathias Jackermeier, Jiaoyan Chen, Ian Horrocks
Venue: WWW
Year: 2024

OWL ontologies, whose formal semantics are rooted in Description Logic (DL), have been widely used for knowledge representation. Similar to Knowledge Graphs (KGs), ontologies are often incomplete, and maintaining and constructing them has proved challenging. While classical deductive reasoning algorithms use the precise formal semantics of an ontology to predict missing facts, recent years have witnessed growing interest in inductive reasoning techniques that can derive probable facts from an ontology. Similar to KGs, a promising approach is to learn ontology embeddings in a latent vector space, while additionally ensuring they adhere to the semantics of the underlying DL. While a variety of approaches have been proposed, current ontology embedding methods suffer from several shortcomings, especially that they all fail to faithfully model one-to-many, many-to-one, and many-to-many relations and role inclusion axioms. To address this problem and improve ontology completion performance, we propose a novel ontology embedding method named Box2EL for the DL EL++, which represents both concepts and roles as boxes (i.e., axis-aligned hyperrectangles), and models inter-concept relationships using a bumping mechanism. We theoretically prove the soundness of Box2EL and conduct an extensive experimental evaluation, achieving state-of-the-art results across a variety of datasets on the tasks of subsumption prediction, role assertion prediction, and approximating deductive reasoning.
Improving Search for New Product Categories via Synthetic Query Generation Strategies
Authors: Akshay Jagatap, Srujana Merugu, Prakash Mandayam Comar
Venue: WWW
Year: 2024

Efficient retrieval and ranking of relevant products in e-commerce product search relies on accurate mapping of queries to product categories. This query classification typically utilizes a combination of textual and customer behavioral signals. However, new product categories often lack customer interaction data leading to poor performance. In this paper, we present a novel approach to mitigate this cold start problem in product ranking via synthetic generation of queries as well as simulation of customer interactions. Specifically we study two strategies for synthetic data generation: (i) fine-tuning a generative language model (LLM) on historical product-query interactions and using it to generate synthetic queries from the product catalog, (ii) Bayesian prompt optimization with an instruction-tuned LLM to directly generate queries from catalog. Empirical evaluation of the proposed approaches on public datasets and real-world customer queries demonstrates significant benefits (+2.96% and +2.34% in PR-AUC on e-commerce queries)1 relative to the baseline approach without synthetic data augmentation. Furthermore, evaluation of the augmented model on live search page results in a substantial increase in highly relevant product results (+3.35%) and reduction (-3.07%) in irrelevant results.
Knowledge Enabled Relation Extraction
Authors: Monika Jain
Venue: WWW
Year: 2024

Relation extraction is the task of extracting relationships from input text, where input can be a sentence, document, or multiple documents. This task has been popular for decades and is still of keen interest. Various techniques have been proposed to solve the relation extraction problem, among which the most popular are using distant supervision, deep learning-based models, reasoning-based models, and transformer-based models. We propose three approaches (named ReOnto, DocRE-CLip, and KDocRE) for relation extraction from text at three levels of granularity (sentence, document and across documents). These approaches embed knowledge in a deep learning based model to improve performance. ReOnto and DocRE-CLip have been evaluated and the source code is publicly available. We are currently implementing and evaluating KDocRE.
Counterfactual Explanations for Visual Recommender Systems
Authors: Neham Jain, Vibhhu Sharma, Gaurav Sinha
Venue: WWW
Year: 2024

Users rely on clever recommendations for items they might like to buy, and service providers rely on clever recommender systems to ensure that their product is recommended to their target audience. Providing explanations for recommendations helps to increase transparency and the users' overall trust in the system, besides helping practitioners debug their recommendation model. Modern recommendation systems utilize multi-modal data such as reviews and images to provide recommendation. In this work, we propose CAVIAR (Counterfactual explanations for VIsual Recommender systems), a novel method to explain recommender systems that utilize visual features of items. Our explanation is counterfactual and is optimized to be simultaneously simple and effective. Given an item in the user's top-K recommended list, CAVIAR makes a minimal, yet meaningful, perturbation to the item's image-embedding such that it is no longer a part of the list. In this way, CAVIAR aims to find the visual features of the item that were the most relevant for the recommendation. In order to lend meaning to the perturbations, we leverage CLIP model to connect the perturbed image features to textual features. We frame the explanation as a natural language counterfactual by contrasting the observed visual features in the item before and after the perturbation.
Meet Challenges of RTT Jitter, A Hybrid Internet Congestion Control Algorithm
Authors: Lianchen Jia, Chao Zhou, Tianchi Huang, Chaoyang Li, Lifeng Sun
Venue: WWW
Year: 2024

Congestion control has been a fundamental research focus in web transmission for over 30 years. However, with diverse network scenarios like cellular networks and WiFi, traditional models might no longer accurately describe current network conditions -- we empirically observe that the minimum round-trip time (RTTmin) still varies under different network conditions, challenging the assumption of its constancy in traditional models. In this paper, we model it as a normal distribution based on our measurements and propose a novel congestion control algorithm LingBo. LingBo consists of two phases: an offline trained decision model to achieve goals under different RTTmin distributions, and an online perception scheme to detect the current RTTmin distribution. We evaluate LingBo in various network environments and find it consistently performs well in terms of power metric and throughput compared to recent state-of-the-art baselines. Our code is available at https://github.com/thumedia/LingBo.