OpenRaiser/Intern-Atlas huggingface dataset:
Semantic-aware node synthesis for imbalanced heterogeneous information networks
Authors: Xinyi Gao, Wentao Zhang, Tong Chen, Junliang Yu, Nguyen Quoc Viet Hung, Hongzhi Yin
Venue: Conference CIKM 2023
Year: 2023

Heterogeneous graph neural networks (HGNNs) have exhibited exceptional efficacy in modeling the complex heterogeneity in heterogeneous information networks (HINs). The critical advantage of HGNNs is their ability to handle diverse node and edge types in HINs by extracting and utilizing the abundant semantic information for effective representation learning. As a widespread phenomenon in many real-world scenarios, the class-imbalance distribution in HINs creates a performance bottleneck for existing HGNNs. Apart from the node imbalance in quantity, the more crucial and distinctive challenge in HINs is semantic imbalance. Minority classes in HINs often lack diverse and sufficient neighbor nodes, resulting in biased and incomplete semantic information. This semantic imbalance further compounds the difficulty of accurately classifying minority nodes, leading to the performance degradation of HGNNs. However, existing remedies are either tailored for non-graph data or designed specifically for homogeneous graphs, thus overlooking the inherent semantic imbalance in HINs. To tackle the imbalance of minority classes and supplement their inadequate semantics, we present the first method for the semantic imbalance problem in imbalanced HINs named Semantic-aware Node Synthesis (SNS). By assessing the influence on minority classes, SNS adaptively selects the heterogeneous neighbor nodes and augments the network with synthetic nodes while preserving the minority semantics. In addition, we introduce two dedicated regularization approaches for HGNNs that explore the inter-type and intra-type information and constrain the representation of synthetic nodes from both semantic and class perspectives to effectively suppress the potential noises from synthetic nodes, facilitating more expressive embeddings for classification. The comprehensive experimental study demonstrates that SNS consistently outperforms existing methods in different benchmark datasets.
Cashing in on Contacts: Characterizing the OnlyFans Ecosystem
Authors: Pelayo Vallina, Ignacio Castro, Gareth Tyson
Venue: Conference WWW 2023
Year: 2023

Adult video-sharing has undergone dramatic shifts. New platforms that directly interconnect (often amateur) producers and consumers now allow content creators to promote material across the web and directly monetize the content they produce. OnlyFans is the most prominent example of this new trend. OnlyFans is a content subscription service where creators earn money from users who subscribe to their material. In contrast to prior adult platforms, OnlyFans emphasizes creator-consumer interaction for audience accumulation and maintenance. This results in a wide cross-platform ecosystem geared towards bringing consumers to creators’ accounts. In this paper, we inspect this emerging ecosystem, focusing on content creators and the third-party platforms they connect to.
User Donations in Online Social Game Streaming: The Case of Paid Subscription in Twitch.tv
Authors: Jiajun Yu, Adele Lu Jia
Venue: Conference WWW 2022
Year: 2022

Online social game streaming has proliferated with the rise of communities like Twitch.tv and Youtube Gaming. Beyond entertainment, they become vibrant communities for streamers and viewers to interact and support each other, and the phenomenon of user donation is rapidly emerging in these communities. In this article, we provide a publicly available (anonymized) dataset and conduct an in-depth analysis of user donations (made through paid user subscriptions) on Twitch, a worldwide popular online social game streaming community. Based on information of over 2.77 million subscription relationships that worth in total over 14.1 million US dollars, we reveal the scale and diversity of paid user subscriptions received and made. Among other results, we find that (i) the paid subscriptions received and made are highly skewed, (ii) majority streamers are casual streamers who only come online occasionally, while regular streamers often stream in multiple categories and receive more paid subscriptions, in total as well as per streaming hour, (iii) a considerable amount of viewers on Twitch subscribe to multiple streamers and most viewers support their streamers moderately while a small amount of devoted fans are willing to pay more and longer. Our discussions and finding shed lights on how to maintain community prosperity and provide significant reference for the system design.
Leveraging member-group relations via multi-view graph filtering for effective group recommendation
Authors: Chae-Hyun Kim, Yoon-Ryung Choi, Jin-Duk Park, Won-Yong Shin
Venue: Conference WWW 2025
Year: 2025

Group recommendation aims at providing optimized recommendations tailored to diverse groups, enabling groups to enjoy appropriate items. On the other hand, most existing group recommendation methods are built upon deep neural network (DNN) architectures designed to capture the intricate relationships between member-level and group-level interactions. While these DNN-based approaches have proven their effectiveness, they require complex and expensive training procedures to incorporate group-level interactions in addition to member-level interactions. To overcome such limitations, we introduce Group-GF, a new approach for extremely fast recommendations of items to each group via multi-view graph filtering (GF) that offers a holistic view of complex member--group dynamics, without the need for costly model training. Specifically, in Group-GF, we first construct three item similarity graphs manifesting different viewpoints for GF. Then, we discover a distinct polynomial graph filter for each similarity graph and judiciously aggregate the three graph filters. Extensive experiments demonstrate the effectiveness of Group-GF in terms of significantly reducing runtime and achieving state-of-the-art recommendation accuracy.
Minimum topology attacks for graph neural networks
Authors: Mengmei Zhang, Xiao Wang, Chuan Shi, Lingjuan Lyu, Tianchi Yang, Junping Du
Venue: Conference WWW 2023
Year: 2023

With the great popularity of Graph Neural Networks (GNNs), their robustness to adversarial topology attacks has received significant attention. Although many attack methods have been proposed, they mainly focus on fixed-budget attacks, aiming at finding the most adversarial perturbations within a fixed budget for target node. However, considering the varied robustness of each node, there is an inevitable dilemma caused by the fixed budget, i.e., no successful perturbation is found when the budget is relatively small, while if it is too large, the yielding redundant perturbations will hurt the invisibility. To break this dilemma, we propose a new type of topology attack, named minimum-budget topology attack, aiming to adaptively find the minimum perturbation sufficient for a successful attack on each node. To this end, we propose an attack model, named MiBTack, based on a dynamic projected gradient descent algorithm, which can effectively solve the involving non-convex constraint optimization on discrete topology. Extensive results on three GNNs and four real-world datasets show that MiBTack can successfully lead all target nodes misclassified with the minimum perturbation edges. Moreover, the obtained minimum budget can be used to measure node robustness, so we can explore the relationships of robustness, topology, and uncertainty for nodes, which is beyond what the current fixed-budget topology attacks can offer.
Large language models for intent-driven session recommendations
Authors: Zhu Sun, Hongyang Liu, Xinghua Qu, Kaidong Feng, Yan Wang, Yew Soon Ong
Venue: Conference SIGIR 2024
Year: 2024

The goal of intent-aware session recommendation (ISR) approaches is to capture user intents within a session for accurate next-item prediction. However, the capability of these approaches is limited by assuming all sessions have a uniform and fixed number of intents. In reality, user sessions can vary, where the number of intentions may differ from one to another. Moreover, they can only learn user intents in the latent space, which further restricts the model's transparency. To ease these issues, we propose a simple yet effective paradigm for ISR motivated by the advanced reasoning capability of large language models (LLMs). Specifically, we first create an initial prompt to instruct LLMs to predict the next item by inferring varying user intents reflected in a session. Then, we propose an effective optimization mechanism to automatically optimize prompts with an iterative self-reflection. Finally, we leverage the robust generalizability of LLMs across diverse domains to efficiently select the optimal prompt for ISR. As such, the proposed paradigm effectively guides LLMs to identify varying user intents at a semantic level, thus delivering more accurate and comprehensible recommendations. Extensive experiments on three real-world datasets verify the superiority of our proposed method.
Openp5: An open-source platform for developing, training, and evaluating llm-based recommender systems
Authors: Shuyuan Xu, Wenyue Hua, Yongfeng Zhang
Venue: Conference SIGIR 2024
Year: 2024

In recent years, the integration of Large Language Models (LLMs) into recommender systems has garnered interest among both practitioners and researchers. Despite this interest, the field is still emerging, and the lack of open-source R&D platforms may impede the exploration of LLM-based recommendations. This paper introduces OpenP5, an open-source platform designed as a resource to facilitate the development, training, and evaluation of LLM-based generative recommender systems for research purposes. The platform is implemented using the encoder-decoder LLMs (e.g., T5) and the decoder-only LLMs (e.g., LLaMA-2) across 10 widely recognized public datasets, catering to two fundamental recommendation tasks: sequential and straightforward recommendations. Recognizing the crucial role of item IDs in LLM-based recommendations, we have also incorporated three item indexing methods within the OpenP5 platform: random indexing, sequential indexing and collaborative indexing. Built on the Transformers library, the platform facilitates easy customization of LLM-based recommendations for users. OpenP5 boasts a range of features including extensible data processing, task-centric optimization, comprehensive datasets and checkpoints, efficient acceleration, and standardized evaluations, making it a valuable tool for the implementation and evaluation of LLM-based recommender systems. The open-source code and pre-trained checkpoints for the OpenP5 library are publicly available at https://github.com/agiresearch/OpenP5.
Neural collaborative filtering
Authors: Xiangnan He, Lizi Liao, Hanwang Zhang, Liqiang Nie, Xia Hu, Tat-Seng Chua
Venue: Conference WWW 2017
Year: 2017

In recent years, deep neural networks have yielded immense success on speech recognition, computer vision and natural language processing. However, the exploration of deep neural networks on recommender systems has received relatively less scrutiny. In this work, we strive to develop techniques based on neural networks to tackle the key problem in recommendation --- collaborative filtering --- on the basis of implicit feedback. Although some recent work has employed deep learning for recommendation, they primarily used it to model auxiliary information, such as textual descriptions of items and acoustic features of musics. When it comes to model the key factor in collaborative filtering --- the interaction between user and item features, they still resorted to matrix factorization and applied an inner product on the latent features of users and items. By replacing the inner product with a neural architecture that can learn an arbitrary function from data, we present a general framework named NCF, short for Neural network-based Collaborative Filtering. NCF is generic and can express and generalize matrix factorization under its framework. To supercharge NCF modelling with non-linearities, we propose to leverage a multi-layer perceptron to learn the user-item interaction function. Extensive experiments on two real-world datasets show significant improvements of our proposed NCF framework over the state-of-the-art methods. Empirical evidence shows that using deeper layers of neural networks offers better recommendation performance.
Opinion spam detection in web forum: A real case study
Authors: Yu-Ren Chen, Hsin-Hsi Chen
Venue: Conference WWW 2015
Year: 2015

Opinion spamming refers to the illegal marketing practice which involves delivering commercially advantageous opinions as regular users. In this paper, we conduct a real case study based on a set of internal records of opinion spams leaked from a shady marketing campaign. We explore the characteristics of opinion spams and spammers in a web forum to obtain some insights, including subtlety property of opinion spams, spam post ratio, spammer accounts, first post and replies, submission time of posts, activeness of threads, and collusion among spammers. Then we present features that could be potentially helpful in detecting spam opinions in threads. The results of spam detection on first posts show: (1) spam first posts put more focus on certain topics such as the user experiences' on the promoted items, (2) spam first posts generally use more words and pictures to showcase the promoted items in an attempt to impress people, (3) spam first posts tend to be submitted during work time, and (4) the threads that spam first posts initiate are more active to be placed at striking positions. The spam detection on replies is more challenging. Besides lower spam ratio and less content, replies even do not mention the promoted items. Their major intention is to keep the discussion in a thread alive to attract more attention on it. Submission time of replies, thread activeness, position of replies, and spamicity of first post are more useful than content-based features in spam detection on replies.
A Hierarchical and Disentangling Interest Learning Framework for Unbiased and True News Recommendation
Authors: Shoujin Wang, Wentao Wang, Xiuzhen Zhang 0001, Yan Wang 0002, Huan Liu 0001, Fang Chen 0001
Venue: Conference KDD 2024
Year: 2024

In the era of information explosion, news recommender systems are crucial for users to effectively and efficiently discover their interested news. However, most of the existing news recommender systems face two major issues, hampering recommendation quality. Firstly, they often oversimplify users' reading interests, neglecting their hierarchical nature, spanning from high-level event (e.g., US Election) related interests to low-level news article-specifc interests. Secondly, existing work often assumes a simplistic context, disregarding the prevalence of fake news and political bias under the real-world context. This oversight leads to recommendations of biased or fake news, posing risks to individuals and society. To this end, this paper addresses these gaps by introducing a novel framework, the Hierarchical and Disentangling Interest learning framework (HDInt). HDInt incorporates a hierarchical interest learning module and a disentangling interest learning module. The former captures users' high- and low-level interests, enhancing next-news recommendation accuracy. The latter effectively separates polarity and veracity information from news contents and model them more specifcally, promoting fairness- and truth-aware reading interest learning for unbiased and true news recommendations. Extensive experiments on two real-world datasets demonstrate HDInt's superiority over state-of-the-art news recommender systems in delivering accurate, unbiased, and true news recommendations.
Veracity-aware and event-driven personalized news recommendation for fake news mitigation
Authors: Shoujin Wang, Xiaofei Xu, Xiuzhen Zhang, Yan Wang, Wenzhuo Song
Venue: Conference WWW 2022
Year: 2022

Despite the tremendous efforts by social media platforms and fact-check services for fake news detection, fake news and misinformation still spread wildly on social media platforms (e.g., Twitter). Consequently, fake news mitigation strategies are urgently needed. Most of the existing work on fake news mitigation focuses on the overall mitigation on a whole social network while ignoring developing concrete mitigation strategies to deter individual users from sharing fake news. In this paper, we propose a novel veracity-aware and event-driven recommendation model to recommend personalised corrective true news to individual users for effectively debunking fake news. Our proposed model Rec4Mit (Recommendation for Mitigation) not only effectively captures a user’s current reading preference with a focus on which event, e.g., US election, from her/his recent reading history containing true and/or fake news, but also accurately predicts the veracity (true or fake) of candidate news. As a result, Rec4Mit can recommend the most suitable true news to best match the user’s preference as well as to mitigate fake news. In particular, for those users who have read fake news of a certain event, Rec4Mit is able to recommend the corresponding true news of the same event. Extensive experiments on real-world datasets show Rec4Mit significantly outperforms the state-of-the-art news recommendation methods in terms of the capability to recommend personalized true news for fake news mitigation.
Contrastive domain adaptation for early misinformation detection: A case study on covid-19
Authors: Zhenrui Yue, Huimin Zeng, Ziyi Kou, Lanyu Shang, Dong Wang
Venue: Conference CIKM 2022
Year: 2022

Despite recent progress in improving the performance of misinformation detection systems, classifying misinformation in an unseen domain remains an elusive challenge. To address this issue, a common approach is to introduce a domain critic and encourage domain-invariant input features. However, early misinformation often demonstrates both conditional and label shifts against existing misinformation data (e.g., class imbalance in COVID-19 datasets), rendering such methods less effective for detecting early misinformation. In this paper, we propose contrastive adaptation network for early misinformation detection (CANMD). Specifically, we leverage pseudo labeling to generate high-confidence target examples for joint training with source data. We additionally design a label correction component to estimate and correct the label shifts (i.e., class priors) between the source and target domains. Moreover, a contrastive adaptation loss is integrated in the objective function to reduce the intra-class discrepancy and enlarge the inter-class discrepancy. As such, the adapted model learns corrected class priors and an invariant conditional distribution across both domains for improved estimation of the target data distribution. To demonstrate the effectiveness of the proposed CANMD, we study the case of COVID-19 early misinformation detection and perform extensive experiments using multiple real-world datasets. The results suggest that CANMD can effectively adapt misinformation detection systems to the unseen COVID-19 target domain with significant improvements compared to the state-of-the-art baselines.
CL4CTR: A Contrastive Learning Framework for CTR Prediction
Authors: Fangye Wang, Yingxu Wang, Dongsheng Li, Hansu Gu, Tun Lu, Peng Zhang, Ning Gu
Venue: Conference WSDM 2023
Year: 2023

Many Click-Through Rate (CTR) prediction works focused on designing advanced architectures to model complex feature interactions but neglected the importance of feature representation learning, e.g., adopting a plain embedding layer for each feature, which results in sub-optimal feature representations and thus inferior CTR prediction performance. For instance, low frequency features, which account for the majority of features in many CTR tasks, are less considered in standard supervised learning settings, leading to sub-optimal feature representations. In this paper, we introduce self-supervised learning to produce high-quality feature representations directly and propose a model-agnostic Contrastive Learning for CTR (CL4CTR) framework consisting of three self-supervised learning signals to regularize the feature representation learning: contrastive loss, feature alignment, and field uniformity. The contrastive module first constructs positive feature pairs by data augmentation and then minimizes the distance between the representations of each positive feature pair by the contrastive loss. The feature alignment constraint forces the representations of features from the same field to be close, and the field uniformity constraint forces the representations of features from different fields to be distant. Extensive experiments verify that CL4CTR achieves the best performance on four datasets and has excellent effectiveness and compatibility with various representative baselines.
ApeGNN: Node-Wise Adaptive Aggregation in GNNs for Recommendation
Authors: Dan Zhang, Yifan Zhu, Yuxiao Dong, Yuandong Wang, Wenzheng Feng, Evgeny Kharlamov, Jie Tang
Venue: Conference WWW 2023
Year: 2023

In recent years, graph neural networks (GNNs) have made great progress in recommendation. The core mechanism of GNNs-based recommender system is to iteratively aggregate neighboring information on the user-item interaction graph. However, existing GNNs treat users and items equally and cannot distinguish diverse local patterns of each node, which makes them suboptimal in the recommendation scenario. To resolve this challenge, we present a node-wise adaptive graph neural network framework ApeGNN. ApeGNN develops a node-wise adaptive diffusion mechanism for information aggregation, in which each node is enabled to adaptively decide its diffusion weights based on the local structure (e.g., degree). We perform experiments on six widely-used recommendation datasets. The experimental results show that the proposed ApeGNN is superior to the most advanced GNN-based recommender methods (up to 48.94%), demonstrating the effectiveness of node-wise adaptive aggregation.
An Estimation-Theoretic Framework for Image Flow Computation
Authors: Ajit Singh
Venue: Conference ICCV 1990
Year: 1990

A novel framework for computing image flow from time-varying imagery is described. This framework offers the following principal advantages. First, it allows estimation of certain types of discontinuous flow fields without any prior knowledge about the location of discontinuities. The flow fields thus recovered are not blurred at motion boundaries. Second, covariance matrices (or alternatively, confidence measures) are associated with the estimate of image flow at each stage of computation. The estimation-theoretic nature of the framework and its ability to provide covariance matrices make it very useful in the context of applications such as incremental estimation of scene-depth using techniques based on Kalman filtering. The framework is used to recover image flow from two image sequences. To illustrate an application, the image-flow estimates and their covariance matrices thus obtained are also used to recover scene depth.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">></ETX>
Structure from linear or planar motions
Authors: John Oliensis
Venue: Conference CVPR 1996
Year: 1996

We recently demonstrated a new approach to multiframe structure from motion from point features which, in the appropriate domain, provably reconstructs structure and motion correctly. The algorithm works for general motion and large perspective effects. In this paper, we describe how to adapt our approach to translational motion lying along a line or in a plane, with arbitrary rotations. An analysis of the bas-relief effect for multiple-motion sequences is also presented.