TimeMM: Time-as-Operator Spectral Filtering for Dynamic Multimodal Recommendation
Wei Yang, Rui Zhong, Zihan Lin, Xiaodan Wang, Cheng Chen, Huan Ren, Yao Hu

TL;DR
TimeMM introduces a spectral filtering framework that dynamically models evolving user preferences in multimodal recommendation systems by leveraging temporal kernels and adaptive spectral responses.
Contribution
It proposes a novel time-conditioned spectral filtering approach that captures non-stationary interests and modality-specific temporal sensitivities without explicit eigendecomposition.
Findings
TimeMM outperforms state-of-the-art methods on real-world benchmarks.
It maintains linear-time scalability.
The framework effectively models continuous preference evolution.
Abstract
Multimodal recommendation improves user modeling by integrating collaborative signals with heterogeneous item content. In real applications, user interests evolve over time and exhibit nonstationary dynamics, where different preference factors change at different rates. This challenge is amplified in multimodal settings because visual and textual cues can dominate decisions under different temporal regimes. Despite strong progress, most multimodal recommenders still rely on static interaction graphs or coarse temporal heuristics, which limits their ability to model continuous preference evolution with fine-grained temporal adaptation. To address these limitations, we propose TimeMM, a time-conditioned spectral filtering framework for dynamic multimodal recommendation. TimeMM instantiates Time-as-Operator by mapping interaction recency to a family of parametric temporal kernels that…
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