On large-scale e-commerce platforms with tens of millions of active monthly users, recommending visually similar products is essential for enabling users to efficiently discover items that align with their preferences. This study presents the application of a vision-language model (VLM) -- which has demonstrated strong performance in image recognition and image-text retrieval tasks -- to product recommendations on Mercari, a major consumer-to-consumer marketplace used by more than 20 million monthly users in Japan. Specifically, we fine-tuned SigLIP, a VLM employing a sigmoid-based contrastive loss, using one million product image-title pairs from Mercari collected over a three-month period, and developed an image encoder for generating item embeddings used in the recommendation system. Our evaluation comprised an offline analysis of historical interaction logs and an online A/B test in a production environment. In offline analysis, the model achieved a 9.1% improvement in nDCG@5 compared with the baseline. In the online A/B test, the click-through rate improved by 50% whereas the conversion rate improved by 14% compared with the existing model. These results demonstrate the effectiveness of VLM-based encoders for e-commerce product recommendations and provide practical insights into the development of visual similarity-based recommendation systems.

翻译：在拥有数千万月活跃用户的大规模电商平台上，推荐视觉相似商品对于帮助用户高效发现符合其偏好的商品至关重要。本研究将视觉语言模型应用于日本月活用户超2000万的大型C2C市场Mercari的商品推荐中，该模型在图像识别与图文检索任务中已展现出卓越性能。具体而言，我们使用Mercari平台三个月内收集的百万级商品图像-标题对，对采用基于Sigmoid的对比损失函数的视觉语言模型SigLIP进行微调，并开发了用于生成推荐系统商品嵌入向量的图像编码器。评估工作包括历史交互日志的离线分析与生产环境的在线A/B测试。离线分析显示，该模型在nDCG@5指标上较基线提升9.1%。在线A/B测试中，点击率较现有模型提升50%，转化率提升14%。这些结果证明了基于视觉语言模型的编码器在电商商品推荐中的有效性，并为开发基于视觉相似性的推荐系统提供了实践参考。