Identifying and understanding user intents is a pivotal task for E-Commerce. Despite its popularity, intent understanding has not been consistently defined or accurately benchmarked. In this paper, we focus on predicative user intents as "how a customer uses a product", and pose intent understanding as a natural language reasoning task, independent of product ontologies. We identify two weaknesses of FolkScope, the SOTA E-Commerce Intent Knowledge Graph, that limit its capacity to reason about user intents and to recommend diverse useful products. Following these observations, we introduce a Product Recovery Benchmark including a novel evaluation framework and an example dataset. We further validate the above FolkScope weaknesses on this benchmark.

识别和理解用户意图是电子商务的一个关键任务。本文针对预测用户意图展开研究，将意图理解视为自然语言推理任务，并独立于产品本体论。我们发现SOTA电子商务意图知识图FolkScope存在两个弱点，限制了其对用户意图的推理以及对多样有用产品的推荐能力。基于这些观察，我们引入了一个产品恢复基准，包括一种新颖的评估框架和一个示例数据集，并在该基准上进一步验证了FolkScope的上述弱点。

电子商务中以使用为中心的意图理解