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Found 57 papers, 20 papers with code
Model-Free Counterfactual Subset Selection at Scale
Ensuring transparency in AI decision-making requires interpretable explanations, particularly at the instance level.
Tackling Data Heterogeneity in Federated Time Series Forecasting
Although substantial progress has been made in time series forecasting, most existing methods rely on a centralized training paradigm, where large amounts of data are collected from distributed devices (e. g., sensors, wearables) to a central cloud server.
On-device Content-based Recommendation with Single-shot Embedding Pruning: A Cooperative Game Perspective
To address the inherently high computation costs of Shapley values, we propose an efficient and unbiased method to estimate Shapley values of a CRS's embedding parameters.
Unleashing the Power of Large Language Models for Group POI Recommendations
To this end, we propose a framework that unleashes the power of the Large Language Model (LLM) for context-aware group POI recommendations (LLMGPR).
Scalable and Effective Negative Sample Generation for Hyperedge Prediction
Hyperedge prediction is crucial in hypergraph analysis for understanding complex multi-entity interactions in various web-based applications, including social networks and e-commerce systems.
Instruction-Guided Editing Controls for Images and Multimedia: A Survey in LLM era
The rapid advancement of large language models (LLMs) and multimodal learning has transformed digital content creation and manipulation.
FELLAS: Enhancing Federated Sequential Recommendation with LLM as External Services
The item-level representation service is queried by the central server to enrich the original ID-based item embedding with textual information, while the sequence-level representation service is accessed by each client.
ID-Free Not Risk-Free: LLM-Powered Agents Unveil Risks in ID-Free Recommender Systems
Recent advances in ID-free recommender systems have attracted significant attention for effectively addressing the cold start problem.
Scalable Dynamic Embedding Size Search for Streaming Recommendation
Although a few methods attempt to mitigate this by employing embedding size search strategies to assign different embedding dimensions in streaming recommendations, they assume that the embedding size grows with the frequency of users/items, which eventually still exceeds the predefined memory budget over time.
Heterogeneous Hypergraph Embedding for Recommendation Systems
However, two main challenges persist: i) Neglecting complex higher-order interactions in the KG-based user-item network, potentially leading to sub-optimal recommendations, and ii) Dealing with the heterogeneous modalities of input sources, such as user-item bipartite graphs and KGs, which may introduce noise and inaccuracies.
A Thorough Performance Benchmarking on Lightweight Embedding-based Recommender Systems
State-of-the-art RSs primarily depend on categorical features, which ecoded by embedding vectors, resulting in excessively large embedding tables.
PTF-FSR: A Parameter Transmission-Free Federated Sequential Recommender System
Recently, due to increasing concerns about user data privacy, some researchers have implemented federated learning for sequential recommendation, a. k. a., Federated Sequential Recommender Systems (FedSeqRecs), in which a public sequential recommender model is shared and frequently transmitted between a central server and clients to achieve collaborative learning.
Fast-FedUL: A Training-Free Federated Unlearning with Provable Skew Resilience
Federated learning (FL) has recently emerged as a compelling machine learning paradigm, prioritizing the protection of privacy for training data.
Manipulating Recommender Systems: A Survey of Poisoning Attacks and Countermeasures
This survey aims to fill this gap by primarily focusing on poisoning attacks and their countermeasures.
A Survey of Privacy-Preserving Model Explanations: Privacy Risks, Attacks, and Countermeasures
As the adoption of explainable AI (XAI) continues to expand, the urgency to address its privacy implications intensifies.
Robust Federated Contrastive Recommender System against Model Poisoning Attack
In this paper, we introduce a novel contrastive learning framework designed to fully leverage the client's sparse data through embedding augmentation, referred to as CL4FedRec.
Adversarial Item Promotion on Visually-Aware Recommender Systems by Guided Diffusion
Visually-aware recommender systems have found widespread application in domains where visual elements significantly contribute to the inference of users' potential preferences.
Hide Your Model: A Parameter Transmission-free Federated Recommender System
Existing FedRecs generally adhere to a learning protocol in which a central server shares a global recommendation model with clients, and participants achieve collaborative learning by frequently communicating the model's public parameters.
Budgeted Embedding Table For Recommender Systems
Experiments have shown state-of-the-art performance on two real-world datasets when BET is paired with three popular recommender models under different memory budgets.
Accelerating Scalable Graph Neural Network Inference with Node-Adaptive Propagation
To further accelerate Scalable GNNs inference in this inductive setting, we propose an online propagation framework and two novel node-adaptive propagation methods that can customize the optimal propagation depth for each node based on its topological information and thereby avoid redundant feature propagation.
Learning Compact Compositional Embeddings via Regularized Pruning for Recommendation
In addition, we innovatively design a regularized pruning mechanism in CERP, such that the two sparsified meta-embedding tables are encouraged to encode information that is mutually complementary.
Heterogeneous Decentralized Machine Unlearning with Seed Model Distillation
As some recent information security legislation endowed users with unconditional rights to be forgotten by any trained machine learning model, personalized IoT service providers have to put unlearning functionality into their consideration.
Graph Condensation for Inductive Node Representation Learning
To overcome this issue, we propose mapping-aware graph condensation (MCond), explicitly learning the one-to-many node mapping from original nodes to synthetic nodes to seamlessly integrate new nodes into the synthetic graph for inductive representation learning.
Towards Few-shot Inductive Link Prediction on Knowledge Graphs: A Relational Anonymous Walk-guided Neural Process Approach
Previous methods are limited to transductive scenarios, where entities exist in the knowledge graphs, so they are unable to handle unseen entities.
Do as I can, not as I get
This paper proposes a model called TMR to mine valuable information from simulated data environments.
Structure-free Graph Condensation: From Large-scale Graphs to Condensed Graph-free Data
Specifically, SFGC contains two collaborative components: (1) a training trajectory meta-matching scheme for effectively synthesizing small-scale graph-free data; (2) a graph neural feature score metric for dynamically evaluating the quality of the condensed data.
Manipulating Visually-aware Federated Recommender Systems and Its Countermeasures
Therefore, when incorporating visual information in FedRecs, all existing model poisoning attacks' effectiveness becomes questionable.
Explicit Knowledge Graph Reasoning for Conversational Recommendation
Recent conversational recommender systems (CRSs) tackle those limitations by enabling recommender systems to interact with the user to obtain her/his current preference through a sequence of clarifying questions.
Joint Semantic and Structural Representation Learning for Enhancing User Preference Modelling
Knowledge graphs (KGs) have become important auxiliary information for helping recommender systems obtain a good understanding of user preferences.
DREAM: Adaptive Reinforcement Learning based on Attention Mechanism for Temporal Knowledge Graph Reasoning
Although existing TKG reasoning methods have the ability to predict missing future events, they fail to generate explicit reasoning paths and lack explainability.
Manipulating Federated Recommender Systems: Poisoning with Synthetic Users and Its Countermeasures
To reveal the real vulnerability of FedRecs, in this paper, we present a new poisoning attack method to manipulate target items' ranks and exposure rates effectively in the top-$K$ recommendation without relying on any prior knowledge.
High Accurate and Explainable Multi-Pill Detection Framework with Graph Neural Network-Assisted Multimodal Data Fusion
We then offer a framework for integrating a priori with pills' visual features to enhance detection accuracy.
TinyAD: Memory-efficient anomaly detection for time series data in Industrial IoT
With the prevalent deployment of the Industrial Internet of Things (IIoT), an enormous amount of time series data is collected to facilitate machine learning models for anomaly detection, and it is of the utmost importance to directly deploy the trained models on the IIoT devices.
Semi-decentralized Federated Ego Graph Learning for Recommendation
In light of this, we propose a semi-decentralized federated ego graph learning framework for on-device recommendations, named SemiDFEGL, which introduces new device-to-device collaborations to improve scalability and reduce communication costs and innovatively utilizes predicted interacted item nodes to connect isolated ego graphs to augment local subgraphs such that the high-order user-item collaborative information could be used in a privacy-preserving manner.
Interaction-level Membership Inference Attack Against Federated Recommender Systems
An interaction-level membership inference attacker is first designed, and then the classical privacy protection mechanism, Local Differential Privacy (LDP), is adopted to defend against the membership inference attack.
Efficient Integration of Multi-Order Dynamics and Internal Dynamics in Stock Movement Prediction
Advances in deep neural network (DNN) architectures have enabled new prediction techniques for stock market data.
Efficient Graph Neural Network Inference at Large Scale
Graph neural networks (GNNs) have demonstrated excellent performance in a wide range of applications.
Joint Multilingual Knowledge Graph Completion and Alignment
The proposed model combines two components that jointly accomplish KG completion and alignment.
Ranked #1 on
Knowledge Graph Completion
on DPB-5L (French)
Efficient On-Device Session-Based Recommendation
Specifically, each item is represented by a compositional code that consists of several codewords, and we learn embedding vectors to represent each codeword instead of each item.
A Survey of Machine Unlearning
Specifically, as a category collection of cutting-edge studies, the intention behind this article is to serve as a comprehensive resource for researchers and practitioners seeking an introduction to machine unlearning and its formulations, design criteria, removal requests, algorithms, and applications.
Model-Agnostic and Diverse Explanations for Streaming Rumour Graphs
Since the spread of rumours in social media is commonly modelled using feature-annotated graphs, we propose a query-by-example approach that, given a rumour graph, extracts the $k$ most similar and diverse subgraphs from past rumours.
Time-aware Dynamic Graph Embedding for Asynchronous Structural Evolution
Meanwhile, we treat each edge sequence as a whole and embed its ToV of the first vertex to further encode the time-sensitive information.
Detecting Rumours with Latency Guarantees using Massive Streaming Data
Today's social networks continuously generate massive streams of data, which provide a valuable starting point for the detection of rumours as soon as they start to propagate.
Thinking inside The Box: Learning Hypercube Representations for Group Recommendation
Such user and group preferences are commonly represented as points in the vector space (i. e., embeddings), where multiple user embeddings are compressed into one to facilitate ranking for group-item pairs.
Personalized On-Device E-health Analytics with Decentralized Block Coordinate Descent
Actuated by the growing attention to personal healthcare and the pandemic, the popularity of E-health is proliferating.
Incomplete Knowledge Graph Alignment
Our KG embedding framework exploits two feature channels: transitivity-based and proximity-based.
Are Graph Augmentations Necessary? Simple Graph Contrastive Learning for Recommendation
Contrastive learning (CL) recently has spurred a fruitful line of research in the field of recommendation, since its ability to extract self-supervised signals from the raw data is well-aligned with recommender systems' needs for tackling the data sparsity issue.
A War Beyond Deepfake: Benchmarking Facial Counterfeits and Countermeasures
In recent years, visual forgery has reached a level of sophistication that humans cannot identify fraud, which poses a significant threat to information security.
PipAttack: Poisoning Federated Recommender Systems forManipulating Item Promotion
Evaluations on two real-world datasets show that 1) our attack model significantly boosts the exposure rate of the target item in a stealthy way, without harming the accuracy of the poisoned recommender; and 2) existing defenses are not effective enough, highlighting the need for new defenses against our local model poisoning attacks to federated recommender systems.
DA-GCN: A Domain-aware Attentive Graph Convolution Network for Shared-account Cross-domain Sequential Recommendation
Shared-account Cross-domain Sequential recommendation (SCSR) is the task of recommending the next item based on a sequence of recorded user behaviors, where multiple users share a single account, and their behaviours are available in multiple domains.
Entity Alignment for Knowledge Graphs with Multi-order Convolutional Networks
Knowledge graphs (KGs) have become popular structures for unifying real-world entities by modelling the relationships between them and their attributes.
Ranked #7 on
Entity Alignment
on DBP15k zh-en
(using extra training data)
Artificial Intelligence in the Battle against Coronavirus (COVID-19): A Survey and Future Research Directions
Artificial intelligence (AI) has been applied widely in our daily lives in a variety of ways with numerous success stories.
On Node Features for Graph Neural Networks
Graph neural network (GNN) is a deep model for graph representation learning.
Sequence-Aware Factorization Machines for Temporal Predictive Analytics
As a widely adopted solution, models based on Factorization Machines (FMs) are capable of modelling high-order interactions among features for effective sparse predictive analytics.
Unsupervised Learning of Node Embeddings by Detecting Communities
We provide empirical evidence that the communities learned by DMC are meaningful and that the node embeddings are competitive in different node classification benchmarks.
Deep Learning for Deepfakes Creation and Detection: A Survey
By reviewing the background of deepfakes and state-of-the-art deepfake detection methods, this study provides a comprehensive overview of deepfake techniques and facilitates the development of new and more robust methods to deal with the increasingly challenging deepfakes.
Parallel Computation of Graph Embeddings
We therefore propose a framework for parallel computation of a graph embedding using a cluster of compute nodes with resource constraints.
