TimeMM: Time-as-Operator Spectral Filtering for Dynamic Multimodal Recommendation
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 tim...
| Authors | Wei Yang, Rui Zhong, Zihan Lin et al. |
|---|---|
| Primary category | cs.IR |
| Published | 29 April 2026 |
| Last updated at source | 29 April 2026 |
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