Product search plays an essential role in eCommerce. It was treated as a special type of information retrieval problem. Most existing works make use of historical data to improve the search performance, which do not take the opportunity to ask for user's current interest directly. Some session-aware methods take the user's clicks within the session as implicit feedback, but it is still just a guess on user's preference. To address this problem, recent conversational or question-based search models interact with users directly for understanding the user's interest explicitly. However, most users do not have a clear picture on what to buy at the initial stage. Asking critical attributes that the user is looking for after they explored for a while should be a more efficient way to help them searching for the target items. In this paper, we propose a dual-learning model that hybrids the best from both implicit session feedback and proactively clarifying with users on the most critical questions. We first establish a novel utility score to measure whether a clicked item provides useful information for finding the target. Then we develop the dual Selection Net and Ranking Net for choosing the critical questions and ranking the items. It innovatively links traditional click-stream data and text-based questions together. To verify our proposal, we did extensive experiments on a public dataset, and our model largely outperformed other state-of-the-art methods.

翻译：商品搜索在电子商务中扮演着关键角色，它被视作一种特殊的信息检索问题。现有研究大多利用历史数据提升搜索性能，但未直接询问用户当前兴趣。部分会话感知方法将用户会话中的点击作为隐式反馈，但仍仅是对用户偏好的猜测。为解决此问题，近期基于对话或问题的搜索模型通过直接与用户交互来显式理解用户兴趣。然而，大多数用户在初始阶段并未形成清晰的购买目标。在用户探索一段时间后，询问其关注的关键属性应能更高效地协助用户寻找目标商品。本文提出一种融合隐式会话反馈与主动澄清关键问题的双学习模型：首先建立新颖的效用评分，衡量点击物品对发现目标的有用性；进而开发双重的选择网络与排序网络，分别用于选择关键问题和对物品排序。该模型创新性地将传统点击流数据与文本问题相结合。通过在公开数据集上的广泛实验验证，我们的模型显著优于现有最先进方法。