Nowadays, many platforms provide users with both search and recommendation services as important tools for accessing information. The phenomenon has led to a correlation between user search and recommendation behaviors, providing an opportunity to model user interests in a fine-grained way. Existing approaches either model user search and recommendation behaviors separately or overlook the different transitions between user search and recommendation behaviors. In this paper, we propose a framework named UniSAR that effectively models the different types of fine-grained behavior transitions for providing users a Unified Search And Recommendation service. Specifically, UniSAR models the user transition behaviors between search and recommendation through three steps: extraction, alignment, and fusion, which are respectively implemented by transformers equipped with pre-defined masks, contrastive learning that aligns the extracted fine-grained user transitions, and cross-attentions that fuse different transitions. To provide users with a unified service, the learned representations are fed into the downstream search and recommendation models. Joint learning on both search and recommendation data is employed to utilize the knowledge and enhance each other. Experimental results on two public datasets demonstrated the effectiveness of UniSAR in terms of enhancing both search and recommendation simultaneously. The experimental analysis further validates that UniSAR enhances the results by successfully modeling the user transition behaviors between search and recommendation.

翻译：摘要：如今，许多平台为用户同时提供搜索和推荐服务，作为获取信息的重要工具。这一现象导致用户搜索与推荐行为之间存在关联，为细粒度建模用户兴趣提供了契机。现有方法要么将用户搜索和推荐行为分别建模，要么忽视了用户搜索与推荐行为之间的不同转换类型。本文提出一个名为UniSAR的框架，通过有效建模不同类型细粒度行为转换，为用户提供统一的搜索与推荐服务。具体而言，UniSAR通过三个步骤——提取、对齐和融合——对用户搜索与推荐间的转换行为进行建模。其中，提取阶段通过配备预定义掩码的Transformer实现；对齐阶段采用对比学习将提取的细粒度用户转换行为进行对齐；融合阶段则利用交叉注意力机制整合不同转换类型。为提供统一服务，学习到的表示被输入到下游搜索与推荐模型中。采用搜索与推荐数据上的联合学习方法，以充分利用知识并相互增强。在两个公共数据集上的实验结果表明，UniSAR在同时增强搜索与推荐效果方面具有有效性。实验分析进一步验证，UniSAR通过成功建模用户搜索与推荐间的转换行为来提升结果。