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Found 40 papers, 4 papers with code
Rethinking the Representation in Federated Unsupervised Learning with Non-IID Data
The former indicates that representation collapse in local model will subsequently impact the global model and other local models.
Personalized Behavior-Aware Transformer for Multi-Behavior Sequential Recommendation
To tackle these challenges, we propose a Personalized Behavior-Aware Transformer framework (PBAT) for MBSR problem, which models personalized patterns and multifaceted sequential collaborations in a novel way to boost recommendation performance.
Federated Learning for Short Text Clustering
The robust short text clustering module aims to train an effective short text clustering model with local data in each client.
Learning Uniform Clusters on Hypersphere for Deep Graph-level Clustering
However, most existing graph clustering methods focus on node-level clustering, i. e., grouping nodes in a single graph into clusters.
Making Users Indistinguishable: Attribute-wise Unlearning in Recommender Systems
To address the PoT-AU problem in recommender systems, we design a two-component loss function that consists of i) distinguishability loss: making attribute labels indistinguishable from attackers, and ii) regularization loss: preventing drastic changes in the model that result in a negative impact on recommendation performance.
In-processing User Constrained Dominant Sets for User-Oriented Fairness in Recommender Systems
By combining the fairness loss with the original backbone model loss, we address the UOF issue and maintain the overall recommendation performance simultaneously.
Defending Label Inference Attacks in Split Learning under Regression Setting
As a privacy-preserving method for implementing Vertical Federated Learning, Split Learning has been extensively researched.
Joint Local Relational Augmentation and Global Nash Equilibrium for Federated Learning with Non-IID Data
In server, GNE reaches an agreement among inconsistent and discrepant model deviations from clients to server, which encourages the global model to update in the direction of global optimum without breaking down the clients optimization toward their local optimums.
Freshness or Accuracy, Why Not Both? Addressing Delayed Feedback via Dynamic Graph Neural Networks
In the model training, we propose a novel graph convolutional method named HLGCN, which leverages both high-pass and low-pass filters to deal with conversion and non-conversion relationships.
Decentralized Graph Neural Network for Privacy-Preserving Recommendation
The first stage builds a local inner-item hypergraph for each user and a global inter-user graph.
Federated Large Language Model: A Position Paper
Large scale language models (LLM) have received significant attention and found diverse applications across various domains, but their development encounters challenges in real-world scenarios.
Federated Unlearning via Active Forgetting
To this end, we propose a novel federated unlearning framework based on incremental learning, which is independent of specific models and federated settings.
Federated Learning on Non-iid Data via Local and Global Distillation
In this work, we propose FedND: federated learning with noise distillation.
Robust Representation Learning with Reliable Pseudo-labels Generation via Self-Adaptive Optimal Transport for Short Text Clustering
To tackle the above issues, we propose a Robust Short Text Clustering (RSTC) model to improve robustness against imbalanced and noisy data.
PPGenCDR: A Stable and Robust Framework for Privacy-Preserving Cross-Domain Recommendation
Privacy-preserving cross-domain recommendation (PPCDR) refers to preserving the privacy of users when transferring the knowledge from source domain to target domain for better performance, which is vital for the long-term development of recommender systems.
Selective and Collaborative Influence Function for Efficient Recommendation Unlearning
In this paper, we first identify two main disadvantages of directly applying existing unlearning methods in the context of recommendation, i. e., (i) unsatisfactory efficiency for large-scale recommendation models and (ii) destruction of collaboration across users and items.
Heterogeneous Information Crossing on Graphs for Session-based Recommender Systems
HICG utilizes multiple types of user behaviors in the sessions to construct heterogeneous graphs, and captures users' current interests with their long-term preferences by effectively crossing the heterogeneous information on the graphs.
Making Split Learning Resilient to Label Leakage by Potential Energy Loss
As a practical privacy-preserving learning method, split learning has drawn much attention in academia and industry.
DDGHM: Dual Dynamic Graph with Hybrid Metric Training for Cross-Domain Sequential Recommendation
However, the short interaction sequences limit the performance of existing SR. To solve this problem, we focus on Cross-Domain Sequential Recommendation (CDSR) in this paper, which aims to leverage information from other domains to improve the sequential recommendation performance of a single domain.
HCFRec: Hash Collaborative Filtering via Normalized Flow with Structural Consensus for Efficient Recommendation
The ever-increasing data scale of user-item interactions makes it challenging for an effective and efficient recommender system.
A Survey of Trustworthy Graph Learning: Reliability, Explainability, and Privacy Protection
Deep graph learning has achieved remarkable progresses in both business and scientific areas ranging from finance and e-commerce, to drug and advanced material discovery.
Exploiting Variational Domain-Invariant User Embedding for Partially Overlapped Cross Domain Recommendation
Most of the existing CDR models assume that both the source and target domains share the same overlapped user set for knowledge transfer.
Partial Relaxed Optimal Transport for Denoised Recommendation
The interaction data used by recommender systems (RSs) inevitably include noises resulting from mistaken or exploratory clicks, especially under implicit feedbacks.
Making Recommender Systems Forget: Learning and Unlearning for Erasable Recommendation
The basic idea of most recommender systems is collaborative filtering, but existing MU methods ignore the collaborative information across users and items.
Differential Private Knowledge Transfer for Privacy-Preserving Cross-Domain Recommendation
To this end, PriCDR can not only protect the data privacy of the source domain, but also alleviate the data sparsity of the source domain.
Collaborative Filtering with Attribution Alignment for Review-based Non-overlapped Cross Domain Recommendation
In this paper, we focus on the Review-based Non-overlapped Recommendation (RNCDR) problem.
Leveraging Distribution Alignment via Stein Path for Cross-Domain Cold-Start Recommendation
To address this issue, we propose DisAlign, a cross-domain recommendation framework for the CDCSR problem, which utilizes both rating and auxiliary representations from the source domain to improve the recommendation performance of the target domain.
Beyond Glass-Box Features: Uncertainty Quantification Enhanced Quality Estimation for Neural Machine Translation
Quality Estimation (QE) plays an essential role in applications of Machine Translation (MT).
Towards Secure and Practical Machine Learning via Secret Sharing and Random Permutation
Since our method reduces the cost for element-wise function computation, it is more efficient than existing cryptographic methods.
Multi-Facet Recommender Networks with Spherical Optimization
Metric learning has been proposed to capture user-item interactions from implicit feedback, but existing methods only represent users and items in a single metric space, ignoring the fact that users can have multiple preferences and items can have multiple properties, which leads to potential conflicts limiting their performance in recommendation.
Towards Scalable and Privacy-Preserving Deep Neural Network via Algorithmic-Cryptographic Co-design
In this paper, we propose SPNN - a Scalable and Privacy-preserving deep Neural Network learning framework, from algorithmic-cryptographic co-perspective.
Vertically Federated Graph Neural Network for Privacy-Preserving Node Classification
Recently, Graph Neural Network (GNN) has achieved remarkable progresses in various real-world tasks on graph data, consisting of node features and the adjacent information between different nodes.
Practical Privacy Preserving POI Recommendation
Meanwhile, the public data need to be accessed by all the users are kept by the recommender to reduce the storage costs of users' devices.
Semi-supervised Learning Meets Factorization: Learning to Recommend with Chain Graph Model
The proposed CGM is a combination of Bayesian network and Markov random field.
Adaptive Portfolio by Solving Multi-armed Bandit via Thompson Sampling
Also, by constructing multiple strategic arms, we can obtain the optimal investment portfolio to adapt different investment periods.
Purchase as Reward : Session-based Recommendation by Imagination Reconstruction
Inspired by the prediction error minimization (PEM) and embodied cognition, we propose a simple architecture to augment reward, namely Imagination Reconstruction Network (IRN).
Modeling Dynamic Missingness of Implicit Feedback for Recommendation
Recent studies modeled \emph{exposure}, a latent missingness variable which indicates whether an item is missing to a user, to give each missing entry a confidence of being negative feedback.
FinBrain: When Finance Meets AI 2.0
Artificial intelligence (AI) is the core technology of technological revolution and industrial transformation.
Neural Collaborative Autoencoder
Second, due to the difficulty on training deep neural networks, existing explicit models do not fully exploit the expressive potential of deep learning.
Collaborative Filtering with Social Exposure: A Modular Approach to Social Recommendation
We assume that people get information of products from their online friends and they do not have to share similar preferences, which is less restrictive and seems closer to reality.
