Abstract
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.
Abstract (translated)
识别和理解用户意图是电子商务的重要任务。尽管它很受欢迎，但意图理解并没有始终被明确定义或准确衡量。在本文中，我们关注预测用户意图的“客户如何使用产品”，将意图理解视为自然语言推理任务，独立于产品本体论。我们指出了FolkScope中两个限制其对用户意图进行推理和推荐多样有用产品的弱点，并提出了一个包括新的评估框架和示例数据的产品恢复基准。我们进一步在这个基准上验证了上述FolkScope的弱点。
URL
https://arxiv.org/abs/2402.14901
