Modern online service providers such as online shopping platforms often provide both search and recommendation (S&R) services to meet different user needs. Rarely has there been any effective means of incorporating user behavior data from both S&R services. Most existing approaches either simply treat S&R behaviors separately, or jointly optimize them by aggregating data from both services, ignoring the fact that user intents in S&R can be distinctively different. In our paper, we propose a Search-Enhanced framework for the Sequential Recommendation (SESRec) that leverages users' search interests for recommendation, by disentangling similar and dissimilar representations within S&R behaviors. Specifically, SESRec first aligns query and item embeddings based on users' query-item interactions for the computations of their similarities. Two transformer encoders are used to learn the contextual representations of S&R behaviors independently. Then a contrastive learning task is designed to supervise the disentanglement of similar and dissimilar representations from behavior sequences of S&R. Finally, we extract user interests by the attention mechanism from three perspectives, i.e., the contextual representations, the two separated behaviors containing similar and dissimilar interests. Extensive experiments on both industrial and public datasets demonstrate that SESRec consistently outperforms state-of-the-art models. Empirical studies further validate that SESRec successfully disentangle similar and dissimilar user interests from their S&R behaviors.

翻译：现代在线服务提供商（如在线购物平台）通常同时提供搜索和推荐（S&R）服务，以满足用户的不同需求。然而，鲜有有效手段能够整合来自这两种服务的用户行为数据。现有方法大多要么分别处理S&R行为，要么通过聚合两种服务的数据进行联合优化，却忽视了用户在S&R中的意图可能存在显著差异。在本文中，我们提出了一种用于序列推荐的搜索增强框架（SESRec），该框架通过解耦S&R行为中的相似与不相似表示，利用用户的搜索兴趣来增强推荐。具体而言，SESRec首先基于用户的查询-物品交互对齐查询和物品嵌入，以计算其相似度。两个Transformer编码器用于独立学习S&R行为的上下文表示。随后，设计了一个对比学习任务，监督S&R行为序列中相似与不相似表示的解耦。最后，我们通过注意力机制从三个视角提取用户兴趣，即上下文表示、包含相似兴趣和不相似兴趣的两种分离行为。在工业数据集和公开数据集上的大量实验表明，SESRec持续优于最先进的模型。实证研究进一步验证了SESRec能够成功地从用户的S&R行为中解耦出相似与不相似的用户兴趣。