Heterogeneous Influence Maximization in User Recommendation

TL;DR

This paper introduces HeteroIR and HeteroIM models that enhance user recommendation systems by better estimating influence spread and interaction willingness, leading to improved information propagation and engagement.

Contribution

The paper proposes novel models HeteroIR and HeteroIM that integrate influence maximization with recommendation tasks, addressing limitations of existing methods.

Findings

HeteroIR and HeteroIM outperform state-of-the-art baselines significantly.

Deployment in Tencent's platform shows 8.5% and 10% improvements in online A/B tests.

Models effectively increase influence spread and user interaction willingness.

Abstract

User recommendation systems enhance user engagement by encouraging users to act as inviters to interact with other users (invitees), potentially fostering information propagation. Conventional recommendation methods typically focus on modeling interaction willingness. Influence-Maximization (IM) methods focus on identifying a set of users to maximize the information propagation. However, existing methods face two significant challenges. First, recommendation methods fail to unleash the candidates' spread capability. Second, IM methods fail to account for the willingness to interact. To solve these issues, we propose two models named HeteroIR and HeteroIM. HeteroIR provides an intuitive solution to unleash the dissemination potential of user recommendation systems. HeteroIM fills the gap between the IM method and the recommendation task, improving interaction willingness and maximizing spread coverage. The HeteroIR introduces a two-stage framework to estimate the spread profits. The HeteroIM incrementally selects the most influential invitee to recommend and rerank based on the number of reverse reachable (RR) sets containing inviters and invitees. RR set denotes a set of nodes that can reach a target via propagation. Extensive experiments show that HeteroIR and HeteroIM significantly outperform the state-of-the-art baselines with the p-value < 0.05. Furthermore, we have deployed HeteroIR and HeteroIM in Tencent's online gaming platforms and gained an 8.5\% and 10\% improvement in the online A/B test, respectively. Implementation codes are available at https://github.com/socialalgo/HIM.