Conceptualize and Infer User Needs in E-commerce
This addresses the challenge of improving user satisfaction in e-commerce applications by explicitly modeling implicit user needs, though it appears incremental as it builds on existing knowledge graph and deep learning techniques.
The paper tackles the problem of understanding latent user needs in e-commerce by representing them as nodes in a knowledge graph and using a supervised learning algorithm to conceptualize and infer these needs from transaction history, with online tests showing substantial advantages.
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.