个性化产品搜索的动态贝叶斯对比预测编码模型

IF 2.6 4区 计算机科学 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS ACM Transactions on the Web Pub Date : 2023-07-13 DOI:10.1145/3609225
Bin Wu, Zaiqiao Meng, Shangsong Liang
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引用次数: 0

摘要

本文研究了动态个性化产品搜索问题。由于现实世界中的数据稀疏性问题,现有方法面临着数据效率低下的挑战。为了解决这个问题,我们提出了一个动态贝叶斯对比预测编码模型(DBCPC),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,与实体(即用户、产品和单词)的结构信息共同学习动态嵌入。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带来的棘手问题,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。通过这种方式,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,使嵌入更加关注其一般属性,并在偏好随时间演变的过程中捕获一般信息。为了对动态嵌入进行推理,我们提出了一种结合变分目标和对比目标的推理算法。在Amazon数据集上进行了实验,实验结果表明,我们提出的DBCPC可以学习到更高质量的嵌入,并且优于最先进的非动态和动态产品搜索模型。
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Dynamic Bayesian Contrastive Predictive Coding Model for Personalized Product Search
In this paper, 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 the contrastive predictive learning to jointly learn dynamic embeddings with structure information of entities (i.e., users, products and words). Specifically, our DBCPC employs the structured prediction to tackle the intractability caused by non-linear output space and utilizes the time embedding technique to avoid designing different encoders for 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.
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来源期刊
ACM Transactions on the Web
ACM Transactions on the Web 工程技术-计算机:软件工程
CiteScore
4.90
自引率
0.00%
发文量
26
审稿时长
7.5 months
期刊介绍: Transactions on the Web (TWEB) is a journal publishing refereed articles reporting the results of research on Web content, applications, use, and related enabling technologies. Topics in the scope of TWEB include but are not limited to the following: Browsers and Web Interfaces; Electronic Commerce; Electronic Publishing; Hypertext and Hypermedia; Semantic Web; Web Engineering; Web Services; and Service-Oriented Computing XML. In addition, papers addressing the intersection of the following broader technologies with the Web are also in scope: Accessibility; Business Services Education; Knowledge Management and Representation; Mobility and pervasive computing; Performance and scalability; Recommender systems; Searching, Indexing, Classification, Retrieval and Querying, Data Mining and Analysis; Security and Privacy; and User Interfaces. Papers discussing specific Web technologies, applications, content generation and management and use are within scope. Also, papers describing novel applications of the web as well as papers on the underlying technologies are welcome.
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