In this article, we study the problem of dynamic personalized product search. Due to the data-sparsity problem in the real world, existing methods suffer from the challenge of data inefficiency. We address the challenge by proposing a Dynamic Bayesian Contrastive Predictive Coding model (DBCPC), which aims to capture the rich structured information behind search records to improve data efficiency. Our proposed DBCPC utilizes contrastive predictive learning to jointly learn dynamic embeddings with structure information of entities (i.e., users, products, and words). Specifically, our DBCPC employs structured prediction to tackle the intractability caused by non-linear output space and utilizes the time embedding technique to avoid designing different encoders each time in the Dynamic Bayesian models. In this way, our model jointly learns the underlying embeddings of entities (i.e., users, products, and words) via prediction tasks, which enables the embeddings to focus more on their general attributes and capture the general information during the preference evolution with time. For inferring the dynamic embeddings, we propose an inference algorithm combining the variational objective and the contrastive objectives. Experiments were conducted on an Amazon dataset and the experimental results show that our proposed DBCPC can learn the higher-quality embeddings and outperforms the state-of-the-art non-dynamic and dynamic models for product search.

在本文中，我们研究动态个性化产品搜索问题。由于现实世界中的数据稀疏问题，现有方法面临数据效率低下的挑战。我们通过提出动态贝叶斯对比预测编码模型（DBCPC）来应对这一挑战，该模型旨在捕获搜索记录背后丰富的结构化信息，以提高数据效率。我们提出的 DBCPC 利用对比预测学习来联合学习具有实体（即用户、产品和单词）结构信息的动态嵌入。具体来说，我们的DBCPC采用结构化预测来解决非线性输出空间引起的棘手问题，并利用时间嵌入技术来避免在动态贝叶斯模型中每次设计不同的编码器。这样，我们的模型通过预测任务共同学习实体（即用户、产品和单词）的底层嵌入，这使得嵌入能够更多地关注其一般属性，并在偏好随时间演变过程中捕获一般信息。为了推断动态嵌入，我们提出了一种结合变分目标和对比目标的推理算法。在亚马逊数据集上进行了实验，实验结果表明，我们提出的 DBCPC 可以学习更高质量的嵌入，并且优于最先进的产品搜索非动态和动态模型。这使得嵌入能够更多地关注其一般属性，并在偏好随着时间的演变过程中捕获一般信息。为了推断动态嵌入，我们提出了一种结合变分目标和对比目标的推理算法。在亚马逊数据集上进行了实验，实验结果表明，我们提出的 DBCPC 可以学习更高质量的嵌入，并且优于最先进的产品搜索非动态和动态模型。这使得嵌入能够更多地关注其一般属性，并在偏好随着时间的演变过程中捕获一般信息。为了推断动态嵌入，我们提出了一种结合变分目标和对比目标的推理算法。在亚马逊数据集上进行了实验，实验结果表明，我们提出的 DBCPC 可以学习更高质量的嵌入，并且优于最先进的产品搜索非动态和动态模型。