Tootfinder

Contrastive Matrix Completion with Denoising and Augmented Graph Views for Robust Recommendation
Narges Nemati, Mostafa Haghir Chehreghani
https://arxiv.org/abs/2506.10658

Contrastive Matrix Completion with Denoising and Augmented Graph Views for Robust Recommendation
Matrix completion is a widely adopted framework in recommender systems, as predicting the missing entries in the user-item rating matrix enables a comprehensive understanding of user preferences. However, current graph neural network (GNN)-based approaches are highly sensitive to noisy or irrelevant edges--due to their inherent message-passing mechanisms--and are prone to overfitting, which limits their generalizability. To overcome these challenges, we propose a novel method called Matrix Comp…

This https://arxiv.org/abs/2505.20730 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csIR_…

What LLMs Miss in Recommendations: Bridging the Gap with Retrieval-Augmented Collaborative Signals
User-item interactions contain rich collaborative signals that form the backbone of many successful recommender systems. While recent work has explored the use of large language models (LLMs) for recommendation, it remains unclear whether LLMs can effectively reason over this type of collaborative information. In this paper, we conduct a systematic comparison between LLMs and classical matrix factorization (MF) models to assess LLMs' ability to leverage user-item interaction data. We further in…

Communication-Efficient Publication of Sparse Vectors under Differential Privacy
Quentin Hillebrand, Vorapong Suppakitpaisarn, Tetsuo Shibuya
https://arxiv.org/abs/2506.20234

Communication-Efficient Publication of Sparse Vectors under Differential Privacy
In this work, we propose a differentially private algorithm for publishing matrices aggregated from sparse vectors. These matrices include social network adjacency matrices, user-item interaction matrices in recommendation systems, and single nucleotide polymorphisms (SNPs) in DNA data. Traditionally, differential privacy in vector collection relies on randomized response, but this approach incurs high communication costs. Specifically, for a matrix with $N$ users, $n$ columns, and $m$ nonzero …

Knowledge-aware Diffusion-Enhanced Multimedia Recommendation
Xian Mo, Fei Liu, Rui Tang, Jintao, Gao, Hao Liu
https://arxiv.org/abs/2507.16396 https://…

Knowledge-aware Diffusion-Enhanced Multimedia Recommendation
Multimedia recommendations aim to use rich multimedia content to enhance historical user-item interaction information, which can not only indicate the content relatedness among items but also reveal finer-grained preferences of users. In this paper, we propose a Knowledge-aware Diffusion-Enhanced architecture using contrastive learning paradigms (KDiffE) for multimedia recommendations. Specifically, we first utilize original user-item graphs to build an attention-aware matrix into graph neural …