Search papers, labs, and topics across Lattice.
This paper introduces TimeMM, a novel time-conditioned spectral filtering framework for dynamic multimodal recommendation that addresses the challenge of non-stationary user interests in multimodal settings. TimeMM uses interaction recency to parameterize temporal kernels that reweight user-item graph edges and introduces adaptive spectral filtering to capture non-stationary interests, along with spectral-aware modality routing to calibrate visual and textual contributions. Experiments on real-world benchmarks show TimeMM outperforms state-of-the-art multimodal recommenders while maintaining linear-time scalability.
Forget static graphs: TimeMM dynamically reweights user-item interactions based on recency and modality, adapting to evolving user preferences in multimodal recommendations.
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 reweight edges on the user--item graph, producing component-specific representations without explicit eigendecomposition. To capture non-stationary interests, we introduce Adaptive Spectral Filtering that mixes the operator bank according to temporal context, yielding prediction-specific effective spectral responses. To account for modality-specific temporal sensitivity, we further propose Spectral-Aware Modality Routing that calibrates visual and textual contributions conditioned on the same temporal context. Finally, a ranking-space Spectral Diversity Regularization encourages complementary expert behaviors and prevents filter-bank collapse. Extensive experiments on real-world benchmarks demonstrate that TimeMM consistently outperforms state-of-the-art multimodal recommenders while maintaining linear-time scalability.
