Kavli Affiliate: Dan Luo
| First 5 Authors: Feng Liu, Lixin Zou, Xiangyu Zhao, Min Tang, Liming Dong
| Summary:
Generative models, particularly diffusion model, have emerged as powerful
tools for sequential recommendation. However, accurately modeling user
preferences remains challenging due to the noise perturbations inherent in the
forward and reverse processes of diffusion-based methods. Towards this end,
this study introduces FMRec, a Flow Matching based model that employs a
straight flow trajectory and a modified loss tailored for the recommendation
task. Additionally, from the diffusion-model perspective, we integrate a
reconstruction loss to improve robustness against noise perturbations, thereby
retaining user preferences during the forward process. In the reverse process,
we employ a deterministic reverse sampler, specifically an ODE-based updating
function, to eliminate unnecessary randomness, thereby ensuring that the
generated recommendations closely align with user needs. Extensive evaluations
on four benchmark datasets reveal that FMRec achieves an average improvement of
6.53% over state-of-the-art methods. The replication code is available at
https://github.com/FengLiu-1/FMRec.
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