In modern commercial search engines and recommendation systems, data from multiple domains is available to jointly train the multi-domain model. Traditional methods train multi-domain models in the multi-task setting, with shared parameters to learn the similarity of multiple tasks, and task-specific parameters to learn the divergence of features, labels, and sample distributions of individual tasks. With the development of large language models, LLM can extract global domain-invariant text features that serve both search and recommendation tasks. We propose a novel framework called S\&R Multi-Domain Foundation, which uses LLM to extract domain invariant features, and Aspect Gating Fusion to merge the ID feature, domain invariant text features and task-specific heterogeneous sparse features to obtain the representations of query and item. Additionally, samples from multiple search and recommendation scenarios are trained jointly with Domain Adaptive Multi-Task module to obtain the multi-domain foundation model. We apply the S\&R Multi-Domain foundation model to cold start scenarios in the pretrain-finetune manner, which achieves better performance than other SOTA transfer learning methods. The S\&R Multi-Domain Foundation model has been successfully deployed in Alipay Mobile Application's online services, such as content query recommendation and service card recommendation, etc.

在现代商业搜索引擎和推荐系统中，数据来自多个领域被用于联合训练多领域模型。使用大型语言模型，提出了一种名为S&R Multi-Domain Foundation的新型框架，其中利用LLM提取领域不变的文本特征，并使用Aspect Gating Fusion将ID特征、领域不变的文本特征和任务特异性稀疏特征合并以获得查询和项的表示。同时，使用领域自适应多任务模块联合训练来自多个搜索和推荐场景的样本，获得多领域基础模型。将S&R Multi-Domain Foundation模型应用于冷启动场景中，以pretrain-finetune方式取得比其他SOTA迁移学习方法更好的性能。S&R Multi-Domain Foundation模型已成功部署在支付宝移动应用的在线服务中，如内容查询推荐和服务卡片推荐等。

冷启动场景下的统一搜索和推荐基础模型