FedUTR: Federated Recommendation with Augmented Universal Textual Representation for Sparse Interaction Scenarios

TL;DR

FedUTR is a federated recommendation method that leverages textual item representations and adaptive modules to improve performance in sparse data scenarios, ensuring privacy and personalization.

Contribution

The paper introduces FedUTR, a novel federated recommendation framework that combines universal textual representations with personalized modules to address data sparsity issues.

Findings

Achieves up to 59% performance improvement over SOTA baselines.

Effectively incorporates textual data to enhance item representation quality.

Provides theoretical convergence guarantees for the proposed method.

Abstract

Federated recommendations (FRs) have emerged as an on-device privacy-preserving paradigm, attracting considerable attention driven by rising demands for data security. Existing FRs predominantly adapt ID embeddings to represent items, making the quality of item embeddings entirely dependent on users' historical behaviors. However, we empirically observe that this pattern leads to suboptimal recommendation performance under high data sparsity scenarios, due to its strong reliance on historical interactions. To address this issue, we propose a novel method named FedUTR, which incorporates item textual representations as a complement to interaction behaviors, aiming to enhance model performance under high data sparsity. Specifically, we utilize textual modality as the universal representation to capture generic item knowledge, and design a Collaborative Information Fusion Module (CIFM) to complement each user's personalized interaction information. Besides, we introduce a Local Adaptation Module (LAM) that adaptively exploits the off-the-shelf local model to efficiently preserve client-specific personalized preferences. Moreover, we propose a variant of FedUTR, termed FedUTR-SAR, which incorporates a sparsity-aware resnet component to granularly balance universal and personalized information. The convergence analysis proves theoretical guarantees for the effectiveness of FedUTR. Extensive experiments on four real-world datasets show that our method achieves superior performance, with improvements of up to 59% across all datasets compared to the SOTA baselines.