Determinantal Point Processes Guided Crowd-wise Mixture-of-Experts for Recommendation in Alipay

Abstract

Facing the challenges of sparsity and long tail in thousands of Mini-apps recommendation scenarios deployed on Alipay platform, there is a great need for a simple, effective and easy-to-deploy industrial solution. To address this issue, we follow the strategy of ‘divide and conquer’ and propose a crowd-based recommendation model by using D eterminantal P oint P rocesse s on C rowd-wise M ixture- o f- E xperts (DPPs-CMoE). Specifically, under the guidance of DPPs-based prototypical tags, the user profiling space is sequentially divided into multiple crowds, with each of them taking on a unique latent specificity; Meanwhile, by treating the modeling of crowd specificity as one of multiple tasks, a crowd-wise architecture is adopted to seamlessly unify the multiple expert networks from the overall user space and the gating network from each of independent crowd spaces. The effectiveness of the proposed method has been illustrated in the experimental results on a mini-apps recommendation scenario deployed in Alipay APPs.