# Heterogeneous Influence Maximization in User Recommendation

arXiv: https://arxiv.org/abs/2508.13517 · HTML: https://mlsanity.com/paper/2508.13517/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 45 references that are in the map.
- First posted on arXiv 19 Aug 2025.

## Builds on (most cited first, 15 of 15)

- [DeepWalk: Online Learning of Social Representations](https://mlsanity.com/paper/1403.6652/) (2014)
- [DeepFM: A Factorization-Machine based Neural Network for CTR Prediction](https://mlsanity.com/paper/1703.04247/) (2017)
- [Deep Interest Network for Click-Through Rate Prediction](https://mlsanity.com/paper/1706.06978/) (2017)
- [Ups and Downs: Modeling the Visual Evolution of Fashion Trends with One-Class Collaborative Filtering](https://mlsanity.com/paper/1602.01585/) (2016)
- [AutoInt: Automatic Feature Interaction Learning via Self-Attentive Neural Networks](https://mlsanity.com/paper/1810.11921/) (2018)
- [DeepInf: Social Influence Prediction with Deep Learning](https://mlsanity.com/paper/1807.05560/) (2018)
- [EulerNet: Adaptive Feature Interaction Learning via Euler's Formula for CTR Prediction](https://mlsanity.com/paper/2304.10711/) (2023)
- [The Anatomy of a Scientific Rumor](https://mlsanity.com/paper/1301.2952/) (2013)
- [Initialization for Network Embedding: A Graph Partition Approach](https://mlsanity.com/paper/1908.10697/) (2019)
- [Large-Scale Network Embedding in Apache Spark](https://mlsanity.com/paper/2106.10620/) (2021)
- [We Know What You Want: An Advertising Strategy Recommender System for Online Advertising](https://mlsanity.com/paper/2105.14188/) (2021)
- [Distributed Algorithms for Fully Personalized PageRank on Large Graphs](https://mlsanity.com/paper/1903.11749/) (2019)
- [Local structure can identify and quantify influential global spreaders in large scale social networks](https://mlsanity.com/paper/1509.03484/) (2015)
- [Capacity Constrained Influence Maximization in Social Networks](https://mlsanity.com/paper/2306.01782/) (2023)
- [FROG: Effective Friend Recommendation in Online Games via Modality-aware User Preferences](https://mlsanity.com/paper/2504.09428/) (2025)

## Built on by (most cited first, 1 of 1)

- [ProRL: Effective Reinforcement Learning for Proactive Recommendation via Rectified Policy Gradient Estimation](https://mlsanity.com/paper/2605.28293/) (2026)

## Abstract (arXiv)

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.