OpenRaiser/Intern-Atlas huggingface dataset:
Entangled bidirectional encoder to autoregressive decoder for sequential recommendation
Authors: Taegwan Kang, Hwanhee Lee, Byeongjin Choe, Kyomin Jung
Venue: Conference SIGIR 2021
Year: 2021

Recently, BERT has shown overwhelming performance in sequential recommendation by using a bidirectional attention mechanism. Although the bidirectional model effectively captures dynamics from user interaction, its training strategy does not fit well to the inference stage in sequential recommendation which generally proceeds in a left-to-right way. To address this problem, we introduce a new recommendation system built upon BART, which is widely used in NLP tasks. BART uses a left-to-right decoder and injects noise into its bidirectional encoder, which can reduce the gap between training and inference. However, direct usage of BART for recommendation system is challenging due to its model property and domain difference. BART is an auto-regressive generative model, and its noising transformation techniques are originally developed for text sequence. In this paper, we present a novel sequential recommendation model, Entangled BART for Recommendation (E-BART4Rec) that entangles bidirectional encoder and auto-regressive decoder with noisy transformations for user interaction. Unlike BART, where the final output only depends on its output of the decoder, E-BART4Rec dynamically integrates the output of the bidirectional encoder and auto-regressive decoder based on a gating mechanism that calculates the importance of each output. We also employ noisy transformation that imitates the real users' behaviors, such as item deletion, item cropping, item reverse, and item infilling, to the input of the encoder. Extensive experiments on widely used real-world datasets demonstrate that our models significantly outperform the baselines.
Web Table Formatting Affects Readability on Mobile Devices
Authors: Christopher Tensmeyer, Zoya Bylinskii, Tianyuan Cai, Dave Miller, Ani Nenkova, Aleena Gertrudes Niklaus, Shaun Wallace
Venue: Conference WWW 2023
Year: 2023

Reading large tables on small mobile screens presents serious usability challenges that can be addressed, in part, by better table formatting. However, there are few evidenced-based guidelines for formatting mobile tables to improve readability. For this work, we first conducted a survey to investigate how people interact with tables on mobile devices and conducted a study with designers to identify which design considerations are most critical. Based on these findings, we designed and conducted three large scale studies with remote crowdworker participants. Across the studies, we analyze over 14,000 trials from 590 participants who each viewed and answered questions about 28 diverse tables rendered in different formats. We find that smaller cell padding and frozen headers lead to faster task completion, and that while zebra striping and row borders do not speed up tasks, they are still subjectively preferred by participants.
Combining implicit and explicit topic representations for result diversification
Authors: Jiyin He, Vera Hollink, Arjen P. de Vries
Venue: Conference SIGIR 2012
Year: 2012

Result diversification deals with ambiguous or multi-faceted queries by providing documents that cover as many subtopics of a query as possible. Various approaches to subtopic modeling have been proposed. Subtopics have been extracted internally, e.g., from retrieved documents, and externally, e.g., from Web resources such as query logs. Internally modeled subtopics are often implicitly represented, e.g., as latent topics, while externally modeled subtopics are often explicitly represented, e.g., as reformulated queries.
Search result diversification based on hierarchical intents
Authors: Sha Hu, Zhicheng Dou, Xiaojie Wang, Tetsuya Sakai, Ji-Rong Wen
Venue: Conference CIKM 2015
Year: 2015

A large percentage of queries issued to search engines are broad or ambiguous. Search result diversification aims to solve this problem, by returning diverse results that can fulfill as many different information needs as possible. Most existing intent-aware search result diversification algorithms formulate user intents for a query as a flat list of subtopics. In this paper, we introduce a new hierarchical structure to represent user intents and propose two general hierarchical diversification models to leverage hierarchical intents. Experimental results show that our hierarchical diversification models outperform state-of-the-art diversification methods that use traditional flat subtopics.
Enhancing diversity, coverage and balance for summarization through structure learning
Authors: Liangda Li, Ke Zhou, Gui-Rong Xue, Hongyuan Zha, Yong Yu
Venue: Conference WWW 2009
Year: 2009

Document summarization plays an increasingly important role with the exponential growth of documents on the Web. Many supervised and unsupervised approaches have been proposed to generate summaries from documents. However, these approaches seldom simultaneously consider summary diversity, coverage, and balance issues which to a large extent determine the quality of summaries. In this paper, we consider extract-based summarization emphasizing the following three requirements: 1) diversity in summarization, which seeks to reduce redundancy among sentences in the summary; 2) sufficient coverage, which focuses on avoiding the loss of the document's main information when generating the summary; and 3) balance, which demands that different aspects of the document need to have about the same relative importance in the summary. We formulate the extract-based summarization problem as learning a mapping from a set of sentences of a given document to a subset of the sentences that satisfies the above three requirements. The mapping is learned by incorporating several constraints in a structure learning framework, and we explore the graph structure of the output variables and employ structural SVM for solving the resulted optimization problem. Experiments on the DUC2001 data sets demonstrate significant performance improvements in terms of F1 and ROUGE metrics.
Hierarchical Query Classification in E-commerce Search
Authors: Bing He, Sreyashi Nag, Limeng Cui, Suhang Wang, Zheng Li, Rahul Goutam, Zhen Li, Haiyang Zhang
Venue: Conference WWW 2024
Year: 2024

E-commerce platforms typically store and structure product information and search data in a hierarchy. Efficiently categorizing user search queries into a similar hierarchical structure is paramount in enhancing user experience on e-commerce platforms as well as news curation and academic research. The significance of this task is amplified when dealing with sensitive query categorization or critical information dissemination, where inaccuracies can lead to considerable negative impacts. The inherent complexity of hierarchical query classification is compounded by two primary challenges: (1) the pronounced class imbalance that skews towards dominant categories, and (2) the inherent brevity and ambiguity of search queries that hinder accurate classification. To address these challenges, we introduce a novel framework that leverages hierarchical information through (i) enhanced representation learning that utilizes the contrastive loss to discern fine-grained instance relationships within the hierarchy, called ''instance hierarchy'', and (ii) a nuanced hierarchical classification loss that attends to the intrinsic label taxonomy, named ''label hierarchy''. Additionally, based on our observation that certain unlabeled queries share typographical similarities with labeled queries, we propose a neighborhood-aware sampling technique to intelligently select these unlabeled queries to boost the classification performance. Extensive experiments demonstrate that our proposed method is better than state-of-the-art (SOTA) on the proprietary Amazon dataset, and comparable to SOTA on the public datasets of Web of Science and RCV1-V2. These results underscore the efficacy of our proposed solution, and pave the path toward the next generation of hierarchy-aware query classification systems.
How Few Davids Improve One Goliath: Federated Learning in Resource-Skewed Edge Computing Environments
Authors: Jiayun Zhang, Shuheng Li, Haiyu Huang, Zihan Wang, Xiaohan Fu, Dezhi Hong, Rajesh K. Gupta, Jingbo Shang
Venue: Conference WWW 2024
Year: 2024

Real-world deployment of federated learning requires orchestrating clients with widely varied compute resources, from strong enterprise-grade devices in data centers to weak mobile and Web-of-Things devices. Prior works have attempted to downscale large models for weak devices and aggregate shared parts among heterogeneous models. A typical architectural assumption is that there are equally many strong and weak devices. In reality, however, we often encounter resource skew where a few (1 or 2) strong devices hold substantial data resources, alongside many weak devices. This poses challenges-the unshared portion of the large model rarely receives updates or gains benefits from weak collaborators. We aim to facilitate reciprocal benefits between strong and weak devices in resource-skewed environments. We propose RecipFL, a novel framework featuring a server-side graph hypernetwork. This hypernetwork is trained to produce parameters for personalized client models adapted to device capacity and unique data distribution. It effectively generalizes knowledge about parameters across different model architectures by encoding computational graphs. Notably, RecipFL is agnostic to model scaling strategies and supports collaboration among arbitrary neural networks. We establish the generalization bound of RecipFL through theoretical analysis and conduct extensive experiments with various model architectures. Results show that RecipFL improves accuracy by 4.5% and 7.4% for strong and weak devices respectively, incentivizing both devices to actively engage in federated learning.
Diffusion Variational Autoencoder for Tackling Stochasticity in Multi-Step Regression Stock Price Prediction
Authors: Kelvin J. L. Koa, Yunshan Ma, Ritchie Ng, Tat-Seng Chua
Venue: Conference CIKM 2023
Year: 2023

Multi-step stock price prediction over a long-term horizon is crucial for forecasting its volatility, allowing financial institutions to price and hedge derivatives, and banks to quantify the risk in their trading books. Additionally, most financial regulators also require a liquidity horizon of several days for institutional investors to exit their risky assets, in order to not materially affect market prices. However, the task of multi-step stock price prediction is challenging, given the highly stochastic nature of stock data. Current solutions to tackle this problem are mostly designed for single-step, classification-based predictions, and are limited to low representation expressiveness. The problem also gets progressively harder with the introduction of the target price sequence, which also contains stochastic noise and reduces generalizability at test-time. To tackle these issues, we combine a deep hierarchical variational-autoencoder (VAE) and diffusion probabilistic techniques to do seq2seq stock prediction through a stochastic generative process. The hierarchical VAE allows us to learn the complex and low-level latent variables for stock prediction, while the diffusion probabilistic model trains the predictor to handle stock price stochasticity by progressively adding random noise to the stock data. To deal with the additional stochasticity in the target price sequence, we also augment the target series with noise via a coupled diffusion process. We then perform a denoising process to "clean" the prediction outputs that were trained on the stochastic target sequence data, which increases the generalizability of the model at test-time. Our Diffusion-VAE (D-Va) model is shown to outperform state-of-the-art solutions in terms of its prediction accuracy and variance. Through an ablation study, we also show how each of the components introduced helps to improve overall prediction accuracy by reducing the data noise. Most importantly, the multi-step outputs can also allow us to form a stock portfolio over the prediction length. We demonstrate the effectiveness of our model outputs in the portfolio investment task through the Sharpe ratio metric and highlight the importance of dealing with different types of prediction uncertainties. Our code can be accessed through https://github.com/koa-fin/dva.
Unveiling the Paradox of NFT Prosperity
Authors: Jintao Huang, Pengcheng Xia, Jiefeng Li, Kai Ma, Gareth Tyson, Xiapu Luo, Lei Wu, Yajin Zhou, Wei Cai, Haoyu Wang
Venue: Conference WWW 2024
Year: 2024

Unlike fungible tokens (e.g., cryptocurrency), a Non-Fungible Token (NFT) is unique and indivisible. As such, they can be used to authenticate ownership of digital assets (e.g., a photo) in a decentralized fashion. Given that NFTs have generated significant media attention since 2021, we perform a large-scale measurement study of the NFT ecosystem. We collect over 242M transfer logs and over 97M marketplace transactions until Aug 1st, 2023, by far the largest NFT dataset, to the best of our knowledge. We characterize the on-chain behavior of NFTs and their trading across five major marketplaces. We find that, although the NFT ecosystem is growing rapidly, it is driven by a relatively small set of dominant centralized players, with suspicious trade activities, e.g., over 23% of the monetary volume is generated by malicious wash trading and the ecosystem has experienced over 157K cases of NFT arbitrage, with a total sum of over \25M profit. Our observations motivate the need for more research efforts in the NFT security analysis.
DenseFlow: Spotting Cryptocurrency Money Laundering in Ethereum Transaction Graphs
Authors: Dan Lin, Jiajing Wu, Yunmei Yu, Qishuang Fu, Zibin Zheng, Changlin Yang
Venue: Conference WWW 2024
Year: 2024

In recent years, money laundering crimes on blockchain, especially on Ethereum, have become increasingly rampant, resulting in substantial losses. The unique features of money laundering on Ethereum, such as decentralization and pseudonymity, pose new challenges for Ethereum anti-money laundering. Specifically, the existence of dense and extensive laundering gangs and intricate multilayered laundering pathways makes it exceptionally challenging for regulators to identify suspicious accounts and trace money flows. To address this issue, we propose an innovative DenseFlow framework that effectively identifies and traces money laundering activities by finding dense subgraphs and applying the maximum flow idea. We conduct multiple experiments on four datasets from Ethereum to validate the effectiveness of our approach. The precision of our DenseFlow is 16.34% higher than the start-of-the-art comparison methods on average, highlighting its distinctive contribution to tackling money laundering issues on blockchain.
Cyclic arbitrage in decentralized exchanges
Authors: Ye Wang, Yan Chen, Haotian Wu, Liyi Zhou, Shuiguang Deng, Roger Wattenhofer
Venue: Conference WWW 2022
Year: 2022

Decentralized Exchanges (DEXes) enable users to create markets for exchanging any pair of cryptocurrencies. The direct exchange rate of two tokens may not match the cross-exchange rate in the market, and such price discrepancies open up arbitrage possibilities with trading through different cryptocurrencies cyclically. In this paper, we conduct a systematic investigation on cyclic arbitrages in DEXes. We propose a theoretical framework for studying cyclic arbitrage. With our framework, we analyze the profitability conditions and optimal trading strategies of cyclic transactions. We further examine exploitable arbitrage opportunities and the market size of cyclic arbitrages with transaction-level data of Uniswap V2. We find that traders have executed 292,606 cyclic arbitrages over eleven months and exploited more than 138 million USD in revenue. However, the revenue of the most profitable unexploited opportunity is persistently higher than 1 ETH (4,000 USD), which indicates that DEX markets may not be efficient enough. By analyzing how traders implement cyclic arbitrages, we find that traders can utilize smart contracts to issue atomic transactions and the atomic implementations could mitigate users’ financial loss in cyclic arbitrage from the price impact.
ARTEMIS: Detecting Airdrop Hunters in NFT Markets with a Graph Learning System
Authors: Chenyu Zhou, Hongzhou Chen, Hao Wu, Junyu Zhang, Wei Cai
Venue: Conference WWW 2024
Year: 2024

As Web3 projects leverage airdrops to incentivize participation, airdrop hunters tactically amass wallet addresses to capitalize on token giveaways. This poses challenges to the decentralization goal. Current detection approaches tailored for cryptocurrencies overlook non-fungible tokens (NFTs) nuances. We introduce ARTEMIS, an optimized graph neural network system for identifying airdrop hunters in NFT transactions. ARTEMIS captures NFT airdrop hunters through: (1) a multimodal module extracting visual and textual insights from NFT metadata using Transformer models; (2) a tailored node aggregation function chaining NFT transaction sequences, retaining behavioral insights; (3) engineered features based on market manipulation theories detecting anomalous trading. Evaluated on decentralized exchange Blur's data, ARTEMIS significantly outperforms baselines in pinpointing hunters. This pioneering computational solution for an emergent Web3 phenomenon has broad applicability for blockchain anomaly detection. The data and code for the paper are accessible at the following link: \hrefhttps://doi.org/10.5281/zenodo.10676801 doi.org/10.5281/zenodo.10676801.
How reliable is your simulator? analysis on the limitations of current llm-based user simulators for conversational recommendation
Authors: Lixi Zhu, Xiaowen Huang, Jitao Sang
Venue: Conference WWW 2024
Year: 2024

Conversational Recommender System (CRS) interacts with users through natural language to understand their preferences and provide personalized recommendations in real-time. CRS has demonstrated significant potential, prompting researchers to address the development of more realistic and reliable user simulators as a key focus. Recently, the capabilities of Large Language Models (LLMs) have attracted a lot of attention in various fields. Simultaneously, efforts are underway to construct user simulators based on LLMs. While these works showcase innovation, they also come with certain limitations that require attention. In this work, we aim to analyze the limitations of using LLMs in constructing user simulators for CRS, to guide future research. To achieve this goal, we conduct analytical validation on the notable work, iEvaLM. Through multiple experiments on two widely-used datasets in the field of conversational recommendation, we highlight several issues with the current evaluation methods for user simulators based on LLMs: (1) Data leakage, which occurs in conversational history and the user simulator's replies, results in inflated evaluation results. (2) The success of CRS recommendations depends more on the availability and quality of conversational history than on the responses from user simulators. (3) Controlling the output of the user simulator through a single prompt template proves challenging. To overcome these limitations, we propose SimpleUserSim, employing a straightforward strategy to guide the topic toward the target items. Our study validates the ability of CRS models to utilize the interaction information, significantly improving the recommendation results.
Mary, mary, quite contrary: Exposing twitter users to contrarian news
Authors: Kiran Garimella, Gianmarco De Francisci Morales, Aristides Gionis, Michael Mathioudakis
Venue: Conference WWW 2017
Year: 2017

Polarized topics often spark discussion and debate on social media. Recent studies have shown that polarized debates have a specific clustered structure in the endorsement net- work, which indicates that users direct their endorsements mostly to ideas they already agree with. Understanding these polarized discussions and exposing social media users to con- tent that broadens their views is of paramount importance. The contribution of this demonstration is two-fold. (i) A tool to visualize retweet networks about controversial issues on Twitter. By using our visualization, users can understand how polarized discussions are shaped on Twitter, and explore the positions of the various actors. (ii) A solution to reduce polarization of such discussions. We do so by exposing users to information which presents a contrarian point of view. Users can visually inspect our recommendations and understand why and how these would play out in terms of the retweet network. Our demo (https://users.ics.aalto.fi/kiran/reducingControversy/ homepage) provides one of the first steps in developing automated tools that help users explore, and possibly escape, their echo chambers. The ideas in the demo can also help content providers design tools to broaden their reach to people with different political and ideological backgrounds.
Memmap: An adaptive and latent memory structure for dynamic graph learning
Authors: Shuo Ji, Mingzhe Liu 0002, Leilei Sun, Chuanren Liu, Tongyu Zhu
Venue: Conference KDD 2024
Year: 2024

Dynamic graph learning has attracted much attention in recent years due to the fact that most of the real-world graphs are dynamic and evolutionary. As a result, many dynamic learning methods have been proposed to cope with the changes of node states over time. Among these studies, a critical issue is how to update the representations of nodes when new temporal events are observed. In this paper, we provide a novel memory structure - Memory Map (MemMap) for this problem. MemMap is an adaptive and evolutionary latent memory space, where each cell corresponds to an evolving "topic" of the dynamic graph. Moreover, the representation of a node is generated from its semantically correlated memory cells, rather than linked neighbors of the node. We have conducted experiments on real-world datasets and compared our method with the SOTA ones. It can be concluded that: 1) By constructing an adaptive and evolving memory structure during the dynamic learning process, our method can capture the dynamic graph changes, and the learned MemMap is actually a compact evolving structure organized according to the latent "topics" of the graph nodes. 2) Our research suggests that it is a more effective and efficient way to generate node representations from a latent semantic space (like MemMap in our method) than from directly connected neighbors (like most of the previous graph learning methods). The reason is that the number of memory cells in latent space could be much smaller than the number of nodes in a real-world graph, and the representation learning process could well balance the global and local message passing by leveraging the semantic similarity of graph nodes via the correlated memory cells.