Search Results for author:
Found 8 papers, 2 papers with code
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.
Augmented Labeled Random Finite Sets and Its Application to Group Target Tracking
Furthermore, by means of the labeled multi-Bernoulli (LMB) filter with the proposed augmented LRFSs, the group structure is iteratively propagated and updated during the tracking process, which achieves the simultaneously estimation of the kinetic states, track label, and the corresponding group information of multiple group targets, and further improves the GTT tracking performance.
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.
Motif-Based Prompt Learning for Universal Cross-Domain Recommendation
By unifying pre-training and recommendation tasks as a common motif-based similarity learning task and integrating adaptable prompt parameters to guide the model in downstream recommendation tasks, MOP excels in transferring domain knowledge effectively.
Manipulating Visually-aware Federated Recommender Systems and Its Countermeasures
Therefore, when incorporating visual information in FedRecs, all existing model poisoning attacks' effectiveness becomes questionable.
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.
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 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.
