With the rise of modern search and recommendation platforms, insufficient collaborative information of cold-start items exacerbates the Matthew effect of existing platform items, challenging platform diversity and becoming a longstanding issue. Existing methods align items' side content with collaborative information to transfer collaborative signals from high-popularity items to cold-start items. However, these methods fail to account for the asymmetry between collaboration and content, nor the fine-grained differences among items. To address these issues, we propose COINS, an item representation enhancement approach based on fused alignment of semantic IDs. Specifically, we use RQ-OPQ encoding to quantize item content and collaborative information, followed by a two-step alignment: RQ encoding transfers shared collaborative signals across items, while OPQ encoding learns differentiated information of items. Comprehensive offline experiments on large-scale industrial datasets demonstrate superiority of COINS, and rigorous online A/B tests confirm statistically significant improvements: item CTR +1.66%, buyers +1.57%, and order volume +2.17%.

翻译：随着现代搜索与推荐平台的兴起，冷启动商品协同信息的不足加剧了平台现有商品的马太效应，对平台多样性构成挑战，成为长期存在的难题。现有方法通过将商品侧信息与协同信息对齐，将高热度商品的协同信号迁移至冷启动商品。然而，这些方法未能充分考虑协同信息与内容信息之间的不对称性，亦未捕捉商品间的细粒度差异。为解决上述问题，本文提出COINS——一种基于语义ID融合对齐的商品表征增强方法。具体而言，我们采用RQ-OPQ编码对商品内容信息与协同信息进行量化，并通过两步对齐实现信息融合：RQ编码迁移商品间共享的协同信号，而OPQ编码则学习商品的差异化信息。基于大规模工业数据集的离线实验验证了COINS的优越性，严格的在线A/B测试进一步证实了统计显著的性能提升：商品点击率提升1.66%，购买用户数增长1.57%，订单量增加2.17%。