Online shopping platforms, such as Amazon and AliExpress, are increasingly prevalent in society, helping customers purchase products conveniently. With recent progress in natural language processing, researchers and practitioners shift their focus from traditional product search to conversational product search. Conversational product search enables user-machine conversations and through them collects explicit user feedback that allows to actively clarify the users' product preferences. Therefore, prospective research on an intelligent shopping assistant via conversations is indispensable. Existing publications on conversational product search either model conversations independently from users, queries, and products or lead to a vocabulary mismatch. In this work, we propose a new conversational product search model, ConvPS, to assist users in locating desirable items. The model is first trained to jointly learn the semantic representations of user, query, item, and conversation via a unified generative framework. After learning these representations, they are integrated to retrieve the target items in the latent semantic space. Meanwhile, we propose a set of greedy and explore-exploit strategies to learn to ask the user a sequence of high-performance questions for conversations. Our proposed ConvPS model can naturally integrate the representation learning of the user, query, item, and conversation into a unified generative framework, which provides a promising avenue for constructing accurate and robust conversational product search systems that are flexible and adaptive. Experimental results demonstrate that our ConvPS model significantly outperforms state-of-the-art baselines.

翻译：随着亚马逊和速卖通等在线购物平台在社会中的日益普及，顾客能够便捷地购买商品。随着自然语言处理领域的最新进展，研究者和实践者的关注点已从传统产品搜索转向对话式产品搜索。对话式产品搜索支持用户与机器进行对话，并通过这些对话收集明确的用户反馈，从而主动澄清用户的产品偏好。因此，通过对话实现智能购物助手的前瞻性研究不可或缺。现有关于对话式产品搜索的文献要么将对话与用户、查询和产品独立建模，要么导致词汇不匹配问题。在本研究中，我们提出了一种新的对话式产品搜索模型ConvPS，以协助用户定位理想商品。该模型首先通过统一的生成式框架进行训练，以联合学习用户、查询、商品和对话的语义表示。习得这些表示后，将其整合以在潜在语义空间中检索目标商品。同时，我们提出了一套贪婪策略和探索-利用策略，以学习向用户提出一系列高性能的对话问题。我们提出的ConvPS模型能够自然地将用户、查询、商品和对话的表示学习整合到统一的生成式框架中，这为构建灵活自适应的精准鲁棒对话式产品搜索系统提供了可行路径。实验结果表明，我们的ConvPS模型显著优于现有最先进的基线方法。