A Usage-centric Take on Intent Understanding in E -Commerce (2024)

Abstract: Identifying and understanding user intents is a pivotal task for E-Commerce. Despite its essential role in product recommendation and business user profiling analysis, 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: category-rigidity and property-ambiguity. They limit its ability to strongly align user intents with products having the most desirable property, and to recommend useful products across diverse categories. Following these observations, we introduce a Product Recovery Benchmark featuring a novel evaluation framework and an example dataset. We further validate the above FolkScope weaknesses on this benchmark. Our code and dataset are available at https://github.com/stayones/Usgae-Centric-Intent-Understanding.

## introduction user intents are a crucial source of information for e-commerce @xcite @xcite . intents reveal users' motivation in e-commerce interactions: suppose a user plans to go for outdoor barbecue, their intent may not refer only to barbeque smoker grills but also to other products that can be useful, such as disposable cutlery or plates. in these cases, traditional product recommendation approaches would fail to handle these queries or to remind customers of the products they may need but have forgotten. intent understanding offers great benefits in recommending distinct products based on common user intents they fulfil. ## usage-centric intent understanding we propose a usage-centric paradigm of intent understanding, focusing on usage user intents and the kinds of useful products, where the goal is to ground usage user intents in kinds of useful products. differently from the "informal queries" in @xcite , and similarly to @xcite , our usage user intents are generic eventualities/situations, independent of product ontologies. we introduce kinds of products as the target granularity level, as it abstracts away the nuanced differences among individual listings, and yields a purely natural language setup, independent of product ontologies. it contains just enough information (category + property) to represent the product listings inside for intent understanding. user intents rarely require combinations of properties in a product category. therefore, to avoid generating factorial numbers of kinds of product, we impose a mild constraint that only one property is specified for each kind of product. we demonstrate the specificity trade-off with an example below: for outdoor barbecues, a stiffbristle scrub brush is useful for cleaning the grease on the grill. to that end, there are many listings of hard-bristle scrubs but the exact choice among them is irrelevant to the user intent and could be identified by downstream recommendation systems using other factors (customer habit, geo-location, etc.). however, the stiff bristle property is essential for a listing to be suitable for outdoor barbecues. in short, grouping based on kinds of products strikes a balance between sparsity that comes with specificity, and ambiguity that comes with generality. ## discussions and conclusion in this paper, we revisit intent understanding from a usage-centric perspective, as a natural language reasoning task, to detect superficially distinct kinds of products useful for common usage intents. we developed a product recovery benchmark, and investigated two weaknesses of the sota folkscope kg in supporting usage-centric intent understanding: property ambiguity and category-rigidity. we advocate for adopting the usage-centric intent understanding paradigm, and for considering user reviews, in addition to co-buy records. desired product properties and their respective intents are likely to co-occur in product reviews, relieving property-ambiguity; the same usage intents tend to be described consistently in user reviews across different categories, relieving category-rigidity. as for future work, one idea is to use our proposed benchmarks to test some entailment graphs in e-commerce. we might further investigate some abstract inference capabilities that are related to conceptual understanding.