User preference modeling is a vital yet challenging problem in personalized product search. In recent years, latent space based methods have achieved state-of-the-art performance by jointly learning semantic representations of products, users, and text tokens. However, existing methods are limited in their ability to model user preferences. They typically represent users by the products they visited in a short span of time using attentive models and lack the ability to exploit relational information such as user-product interactions or item co-occurrence relations. In this work, we propose to address the limitations of prior arts by exploring local and global user behavior patterns on a user successive behavior graph, which is constructed by utilizing short-term actions of all users. To capture implicit user preference signals and collaborative patterns, we use an efficient jumping graph convolution to explore high-order relations to enrich product representations for user preference modeling. Our approach can be seamlessly integrated with existing latent space based methods and be potentially applied in any product retrieval method that uses purchase history to model user preferences. Extensive experiments on eight Amazon benchmarks demonstrate the effectiveness and potential of our approach. The source code is available at \url{https://github.com/floatSDSDS/SBG}.

翻译：用户偏好模型是个人化产品搜索中一个至关重要但又具有挑战性的问题。近年来,以空间为基础的潜在方法通过共同学习产品、用户和文本符号的语义表达方式,实现了最先进的性能。但是,现有方法在模拟用户偏好方面能力有限。它们通常代表用户,因为它们在短时间内使用仔细的模型访问过的产品,缺乏利用用户-产品互动或项目共同关系等相关信息的能力。在这项工作中,我们提议通过在用户连续行为图上探索本地和全球用户行为模式来解决先前艺术的局限性。该图是利用所有用户的短期行动构建的。为了获取隐含用户偏好信号和合作模式,我们使用高效的跳动图解图解来探索高端关系,以丰富用户偏好模型的产品表达方式。我们的方法可以与现有的基于潜在空间的方法紧密结合,并有可能在任何产品检索方法中使用历史来模拟用户偏好。关于八个亚马逊基准的广泛实验显示了我们方法的有效性和潜力。源代码可在\urlhttpsBADSB{G://githus/com.com查阅。