兼顾用户互动意愿与传播能力，提升推荐系统影响力。
Heterogeneous Influence Maximization in User Recommendation
- 分两阶段估计传播收益，释放推荐潜力
- 通过反向可达集增量选人，覆盖更广传播范围
- 腾讯游戏平台实测提升8.5%~10%，效果显著
用户推荐系统通过鼓励用户邀请他人（邀请者）互动来提升参与度，可能促进信息传播。传统推荐方法侧重建模交互意愿，而影响最大化（IM）方法则关注识别最大传播范围的用户群体。现有方法存在两大缺陷：推荐方法未能激发候选用户的传播潜力；IM方法未考虑互动意愿。为此，本文提出HeteroIR和HeteroIM两个模型。HeteroIR采用两阶段框架估算传播收益，有效释放推荐系统的传播潜能；HeteroIM在推荐过程中增量选择最具影响力的被邀请者，基于包含邀请者与被邀请者的反向可达（RR）集数量进行重排序。实验表明，两种模型均显著优于当前最优基线（p < 0.05）。在腾讯在线游戏平台部署后，HeteroIR和HeteroIM分别带来8.5%和10%的转化率提升。代码已开源：https://github.com/socialalgo/HIM。
原文摘要 · Abstract (English)
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.
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