A Usage-centric Take on Intent Understanding in E-Commerce

Anonymous

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 predictive 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 justify the above FolkScope weaknesses on this benchmark.

Paper Type: short

Research Area: NLP Applications

Contribution Types: Model analysis & interpretability, Data resources, Position papers

Languages Studied: English