Conceptualize and Infer User Needs in E-commerce

TL;DR

This paper introduces a knowledge graph-based supervised learning approach to explicitly model and infer latent user needs in e-commerce, enhancing user satisfaction and application performance.

Contribution

It proposes a novel method to represent implicit user needs as concept nodes in a knowledge graph and infers these needs using a deep attentive model, improving understanding of user behavior.

Findings

Effective and stable model demonstrated in offline experiments.

Significant advantages shown in online industry tests.

Enhanced user needs understanding benefits e-commerce applications.

Abstract

Understanding latent user needs beneath shopping behaviors is critical to e-commercial applications. Without a proper definition of user needs in e-commerce, most industry solutions are not driven directly by user needs at current stage, which prevents them from further improving user satisfaction. Representing implicit user needs explicitly as nodes like "outdoor barbecue" or "keep warm for kids" in a knowledge graph, provides new imagination for various e- commerce applications. Backed by such an e-commerce knowledge graph, we propose a supervised learning algorithm to conceptualize user needs from their transaction history as "concept" nodes in the graph and infer those concepts for each user through a deep attentive model. Offline experiments demonstrate the effectiveness and stability of our model, and online industry strength tests show substantial advantages of such user needs understanding.