MPIUR：建模商品信息与用户评论的多模态图推荐技术
A Technique to Model Product Information and User Reviews (MPIUR)
AI 导读
研究者提出多模态图推荐模型 MPIUR，将用户评论编码进异构图、对齐图像与文本表示，并根据物品交互密度适配协同-语义对齐。在 Amazon Reviews 2023 的 Musical Instruments 和 Baby 两个类别上，MPIUR 在整体及稀疏物品上均取得最优 nDCG@10 和 Recall@10，表明在交互历史有限时联合利用评论与多模态物品内容尤其有效。
正文 · 原文
Abstract:Modern e-commerce platforms naturally provide three complementary modalities: textual item descriptions, item images, and user reviews. However, existing multimodal recommendation methods often omit reviews or do not integrate all three modalities within a unified framework. In this paper, we propose MPIUR, a multimodal graph-based recommendation model that encodes reviews in heterogeneous graphs, aligns image and text representations, and adapts collaborative--semantic alignment to item interaction density. Experiments on the Amazon Reviews 2023 Musical Instruments and Baby categories show that MPIUR achieves the best global nDCG@10 and Recall@10 among the evaluated methods on both datasets. It also obtains the best nDCG@10 and Recall@10 on sparse items, indicating that jointly exploiting reviews and multimodal item content is particularly useful when interaction histories are limited.
| Subjects: | Artificial Intelligence (cs.AI); Information Retrieval (cs.IR) |
| ACM classes: | H.3.3; H.3.5 |
| Cite as: | arXiv:2611.13361 [cs.AI] |
| (or arXiv:2611.13361v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2611.13361 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Fajrian Yunus [view email]
[v1]
Mon, 9 Nov 2026 23:36:38 UTC (483 KB)
来源：arXiv cs.AI · arxiv.org