Cross-Domain Sequential Recommendation (CDSR) improves recommendation performance by utilizing information from multiple domains, which contrasts with Single-Domain Sequential Recommendation (SDSR) that relies on a historical interaction within a specific domain. However, CDSR may underperform compared to the SDSR approach in certain domains due to negative transfer, which occurs when there is a lack of relation between domains or different levels of data sparsity. To address the issue of negative transfer, our proposed CDSR model estimates the degree of negative transfer of each domain and adaptively assigns it as a weight factor to the prediction loss, to control gradient flows through domains with significant negative transfer. To this end, our model compares the performance of a model trained on multiple domains (CDSR) with a model trained solely on the specific domain (SDSR) to evaluate the negative transfer of each domain using our asymmetric cooperative network. In addition, to facilitate the transfer of valuable cues between the SDSR and CDSR tasks, we developed an auxiliary loss that maximizes the mutual information between the representation pairs from both tasks on a per-domain basis. This cooperative learning between SDSR and CDSR tasks is similar to the collaborative dynamics between pacers and runners in a marathon. Our model outperformed numerous previous works in extensive experiments on two real-world industrial datasets across ten service domains. We also have deployed our model in the recommendation system of our personal assistant app service, resulting in 21.4% increase in click-through rate compared to existing models, which is valuable to real-world business.

翻译：跨域序列推荐通过利用多个领域的信息来提升推荐性能，这与仅依赖特定领域内历史交互的单域序列推荐形成对比。然而，由于负迁移现象的存在——即当领域间缺乏关联或数据稀疏程度不同时——跨域序列推荐在某些领域可能表现不及单域序列推荐。为解决负迁移问题，我们提出的跨域序列推荐模型估计每个领域的负迁移程度，并自适应地将其作为权重因子分配给预测损失，以控制梯度在具有显著负迁移的领域间的流动。为此，我们的模型通过非对称协同网络，比较在多领域上训练的模型与仅在特定领域上训练的模型的性能，以评估每个领域的负迁移程度。此外，为促进单域序列推荐与跨域序列推荐任务间有价值线索的迁移，我们开发了一种辅助损失函数，该函数在每领域基础上最大化来自两个任务的表示对之间的互信息。这种单域序列推荐与跨域序列推荐任务间的协同学习，类似于马拉松中领跑者与跑者之间的协作动态。在两个真实世界工业数据集上跨越十个服务领域的广泛实验中，我们的模型优于众多先前工作。我们还将该模型部署于个人助手应用服务的推荐系统中，与现有模型相比，点击率提升了21.4%，这对现实商业应用具有重要价值。