How Powerful are Interest Diffusion on Purchasing Prediction: A Case Study of Taocode

TL;DR

This paper explores how interest diffusion via Taocode influences online purchasing behaviors on Taobao, proposing a novel GNN-based model that outperforms existing methods in predicting purchases.

Contribution

It introduces InfNet, a dynamic GNN framework that models interest diffusion from large-scale Taobao data for improved purchase prediction accuracy.

Findings

InfNet outperforms 8 state-of-the-art baselines.

Interest diffusion significantly impacts purchasing behavior.

Large-scale dataset validates the model's effectiveness.

Abstract

A taocode is a kind of specially coded text-link on Taobao(the world's biggest online shopping website), through which users can share messages about products with each other. Analyzing taocodes can potentially facilitate understanding of the social relationships between users and, more excitingly, their online purchasing behaviors under the influence of taocode diffusion. This paper innovatively investigates the problem of online purchasing predictions from an information diffusion perspective, with taocode as a case study. Specifically, we conduct profound observational studies on a large-scale real-world dataset from Taobao, containing over 100M Taocode sharing records. Inspired by our observations, we propose InfNet, a dynamic GNN-based framework that models the information diffusion across Taocode. We then apply InfNet to item purchasing predictions. Extensive experiments on real-world datasets validate the effectiveness of InfNet compared with 8 state-of-the-art baselines.