学习提问：基于贝叶斯的序贯贝叶斯产品搜索
Learning to Ask: Question-based Sequential Bayesian Product Search
摘要：
产品搜索通常被认为是在线购物的第一个也是最重要的阶段，因此对于电子商务的用户和零售商来说意义重大。大多数传统的检索方法使用一些相似度函数来匹配用户的查询和描述产品的文档，无论是直接或在一个潜在的向量空间。然而，用户查询通常过于笼统，无法捕捉用户正在寻找的特定产品的微小细节。在本文中，我们提出了一种新的交互式方法来有效地定位最佳匹配的产品。该方法是基于这样的假设，即有一组候选问题，每个产品要问。在这项工作中，我们实例化这个候选人集的假设，产品可以区分与他们相关的文档中出现的实体。我们提出了一个基于贝叶斯的顺序贝叶斯产品搜索方法，QSBPS，直接查询用户的预期存在的实体在相关的产品文档。该方法学习产品的相关性，以及潜在的问题被问到用户的奖励，通过被训练的搜索历史和购买行为的特定用户与其他用户。实验结果表明，该方法可以大大提高产品搜索的性能相比，最先进的基线。
英文摘要：
Product search is generally recognized as the first and foremost stage of online shopping and thus significant for users and retailers of e-commerce. Most of the traditional retrieval methods use some similarity functions to match the user's query and the document that describes a product, either directly or in a latent vector space. However, user queries are often too general to capture the minute details of the specific product that a user is looking for. In this paper, we propose a novel interactive method to effectively locate the best matching product. The method is based on the assumption that there is a set of candidate questions for each product to be asked. In this work, we instantiate this candidate set by making the hypothesis that products can be discriminated by the entities that appear in the documents associated with them. We propose a Question-based Sequential Bayesian Product Search method, QSBPS, which directly queries users on the expected presence of entities in the relevant product documents. The method learns the product relevance as well as the reward of the potential questions to be asked to the user by being trained on the search history and purchase behavior of a specific user together with that of other users. The experimental results show that the proposed method can greatly improve the performance of product search compared to the state-of-the-art baselines.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
