Disentangled Interest importance aware Knowledge Graph Neural Network for Fund Recommendation

Abstract

At present, people are gradually becoming aware of financial management and thus fund recommendation attracts more and more attention to help them find suitable funds quickly. As a user usually takes many factors (e.g., fund theme, fund manager) into account when investing a fund and the fund usually consists of a substantial collection of investments, effectively modeling multi-interest representations is more crucial for personalized fund recommendation than the traditional goods recommendation. However, existing multi-interest methods are largely sub-optimal for fund recommendation, since they ignore financial domain knowledge and diverse fund investment intentions. In this work, we propose a Disentangled Interest importance aware Knowledge Graph Neural Network (DIKGNN) for personalized fund recommendation on FinTech platforms. In particular, we restrict the multiple intent spaces by introducing the attribute nodes from the fund knowledge graph as the minimum intent modeling unit to utilize financial domain knowledge and provide interpretability. In the intent space, we define disentangled intent representations, equipped with intent importance distributions to describe the diverse fund investment intentions. Then we design a new neighbor aggregation mechanism with the learned intent importance distribution upon the interaction graph and knowledge graph to collect multi-intent information. Furthermore, we leverage micro independence and macro balance constraints on the representations and distributions respectively to encourage intent independence and diversity. The extensive experiments on public recommendation benchmarks demonstrate that DIKGNN can achieve substantial improvement over state-of-the-art methods. Our proposed model is also evaluated over one real-world industrial fund dataset from a FinTech platform and has been deployed online.