弥合信息检索与商品搜索系统之间的鸿沟：电子商务中的问答推荐
Bridging the Gap Between Information Seeking and Product Search Systems: Q&A Recommendation for E-commerce
AI总结：
本文提出在电子商务商品搜索中集成问答推荐，利用LLM弥合信息检索与商品搜索的鸿沟，讨论问答对生成与推荐优化，并指出挑战与未来方向。
摘要：
购物任务中的消费者通常会在迭代过程中同时利用商品搜索和信息检索系统（如网络搜索引擎和问答系统），以加深对可用产品的理解并做出购买决策。商品搜索有助于购物者在目录中找到满足其需求的实际产品，而信息检索系统则可用于解答他们可能提出的问题，以细化这些需求。大型语言模型（LLM）近期的成功为弥合这两项任务之间的鸿沟提供了机遇，通过将对话式问答集成到商品搜索中，帮助客户快速有效地实现目标。在本文中，我们提出向用户推荐与其商品搜索相关且有助于其做出购买决策的问答对。我们讨论了该问题的不同方面，包括问答对的要求与特征、其生成方式以及问答推荐任务的优化。我们强调了挑战、开放性问题以及建议的解决方案，以鼓励这一新兴领域的未来研究。
英文摘要：
Consumers on a shopping mission often leverage both product search and information seeking systems, such as web search engines and Question Answering (QA) systems, in an iterative process to improve their understanding of available products and reach a purchase decision. While product search is useful for shoppers to find the actual products meeting their requirements in the catalog, information seeking systems can be utilized to answer any questions they may have to refine those requirements. The recent success of Large Language Models (LLMs) has opened up an opportunity to bridge the gap between the two tasks to help customers achieve their goals quickly and effectively by integrating conversational QA within product search. In this paper, we propose to recommend users Question-Answer (Q&A) pairs that are relevant to their product search and can help them make a purchase decision. We discuss the different aspects of the problem including the requirements and characteristics of the Q&A pairs, their generation, and the optimization of the Q&A recommendation task. We highlight the challenges, open problems, and suggested solutions to encourage future research in this emerging area.
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。
