Pacer和Runner：单域与跨域序列推荐间的协作学习框架
Pacer and Runner: Cooperative Learning Framework between Single- and Cross-Domain Sequential Recommendation
AI总结：
针对跨域序列推荐中的负迁移问题，提出Pacer和Runner协作学习框架，通过非对称协作网络评估并加权控制负迁移，结合互信息最大化辅助损失，在真实数据集和线上部署中显著提升了推荐点击率。
摘要：
跨域序列推荐（CDSR）通过利用多个域的信息来提升推荐性能，这与依赖于特定域内历史交互的单域序列推荐（SDSR）形成对比。然而，由于域之间缺乏关联或数据稀疏性程度不同所引发的负迁移，CDSR在某些域上的表现可能逊色于SDSR方法。为解决负迁移问题，我们提出的CDSR模型估计每个域的负迁移程度，并将其自适应地分配为预测损失的权重因子，以控制通过具有显著负迁移的域的梯度流。为此，我们的模型利用非对称协作网络，将在多个域上训练的模型（CDSR）与仅在特定域上训练的模型（SDSR）的性能进行比较，以评估每个域的负迁移。此外，为了促进SDSR和CDSR任务之间有价值线索的传递，我们开发了一个辅助损失，在逐域基础上最大化两个任务表示对之间的互信息。SDSR和CDSR任务之间的这种协作学习类似于马拉松中配速员和跑步者之间的协作动态。在两个真实工业数据集的十个服务域上进行的广泛实验中，我们的模型优于众多先前的工作。我们还将该模型部署在个人助手应用服务的推荐系统中，与现有模型相比，点击率提高了21.4%，这对现实商业具有宝贵价值。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
