| 摘要: |
| 随着商家评论网站的快速发展, 网站上的内容越来越多, 用户难以在短时间内获取到有价值的评论. 引入了一项名为“多模态定制化评论生成”的新任务. 该任务旨在为特定用户生成他们尚未评价的产品的定制化评论, 这有助于用户对特定产品提供宝贵的意见. 为了实现这一目标, 探索了一种基于预训练语言模型的多模态评论生成框架. 具体而言, 采用了一种多模态预训练语言模型. 该模型接受产品图片和用户偏好作为输入. 之后对视觉和文本特征进行融合, 从而生成定制化评论. 实验结果表明, 该模型在生成高质量的定制化评论方面具有显著效果. |
| 关键词: 多模态信息 评论生成 预训练模型 注意力机制 |
| DOI：10.13328/j.cnki.jos.007465 |
| 分类号:TP18 |
| 基金项目:国家自然科学基金(62376178); 江苏省高校优势学科项目 |
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| Customized Review Generation Integrating Multimodal Information |
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QIANG Min-Jie, WANG Zhong-Qing, LI Shou-Shan, ZHOU Guo-Dong
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School of Computer Science & Technology, Soochow University, Suzhou 215006, China
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| Abstract: |
| With the rapid development of merchant review websites, the volume of content on these websites has increased significantly, making it challenging for users to quickly find valuable reviews. This study introduces a new task, “multimodal customized review generation”. The task aims to generate customized reviews for specific users about products they have not yet reviewed, thus providing valuable insights into these products. To achieve this goal, this study explores a multimodal review generation framework based on a pre-trained language model. Specifically, a multimodal pre-trained language model is employed, which takes product images and user preferences as inputs. The visual and textual features are then fused to generate customized reviews. Experimental results demonstrate that the proposed model is effective in generating high-quality customized reviews. |
| Key words: multimodal information review generation pre-trained model attention mechanism |
