MMRM: A Multiplex Multimodal Representation Model for Product Ranking in E-commerce Search
Multimodal information is pivotal for e-commerce search ranking. Existing works leverage multimodal data typically by fine-tuning general Multimodal Large Language Models (MLLMs) via collaborative signals, subsequently integrating the derived representations into ranking models as item features. Despite their efficacy, these methods face two primary limitations: (1) they rely on a single collaborative signal for MLLM fine-tuning, failing to exploit the heterogeneous signals essential for multitask ranking; and (2) they treat multimodal representations as regular item features in ranking models, underutilizing their latent potential for user behavior modeling. To address these challenges, ...
| Authors | Zhen-Lin Chen, Maosen Sheng, Peng Lin et al. |
|---|---|
| Primary category | cs.IR |
| Published | 13 July 2026 |
| Last updated at source | 13 July 2026 |
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