Harnessing Large Language Models for Multimodal Product Bundling
<|reference_start|>Harnessing Large Language Models for Multimodal Product Bundling: Product bundling provides clients with a strategic combination of individual items. And it has gained significant attention in recent years as a fundamental prerequisite for online services. Recent methods utilize multimodal information through sophisticated extractors for bundling, but remain limited by inferior semantic understanding, the restricted scope of knowledge, and an inability to handle cold-start issues. Despite the extensive knowledge and complex reasoning capabilities of large language models (LLMs), their direct utilization fails to process multimodalities and exploit their knowledge for multimodal product bundling. Adapting LLMs for this purpose involves demonstrating the synergies among different modalities and designing an effective optimization strategy for bundling, which remains challenging. To this end, we introduce Bundle-LLM to bridge the gap between LLMs and product bundling tasks. Specifically, we utilize a hybrid item tokenization to integrate multimodal information, where a simple yet powerful multimodal fusion module followed by a trainable projector embeds all non-textual features into a single token. This module not only explicitly exhibits the interplays among modalities but also shortens the prompt length, thereby boosting efficiency. By designing a prompt template, we formulate product bundling as a multiple-choice question given candidate items. Furthermore, we adopt progressive optimization strategy to fine-tune the LLMs for disentangled objectives, achieving effective product bundling capability with comprehensive multimodal semantic understanding. Extensive experiments on four datasets from two application domains show that our approach outperforms a range of state-of-the-art (SOTA) methods.<|reference_end|>
@article{liu2024harnessing,
    title={Harnessing Large Language Models for Multimodal Product Bundling},
    author={Xiaohao Liu, Jie Wu, Zhulin Tao, Yunshan Ma, Yinwei Wei, Tat-seng Chua},
    journal={arXiv preprint arXiv:2407.11712},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.11712},
    primaryClass={cs.IR}
}