# FROG: Effective Friend Recommendation in Online Games via Modality-aware User Preferences

arXiv: https://arxiv.org/abs/2504.09428 · HTML: https://mlsanity.com/paper/2504.09428/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 55 references that are in the map.
- First posted on arXiv 13 Apr 2025.

## Builds on (most cited first, 24 of 24)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://mlsanity.com/paper/1905.11946/) (2019)
- [Inductive Representation Learning on Large Graphs](https://mlsanity.com/paper/1706.02216/) (2017)
- [node2vec: Scalable Feature Learning for Networks](https://mlsanity.com/paper/1607.00653/) (2016)
- [DeepWalk: Online Learning of Social Representations](https://mlsanity.com/paper/1403.6652/) (2014)
- [Wide & Deep Learning for Recommender Systems](https://mlsanity.com/paper/1606.07792/) (2016)
- [Graph Neural Networks for Social Recommendation](https://mlsanity.com/paper/1902.07243/) (2019)
- [DeepFM: A Factorization-Machine based Neural Network for CTR Prediction](https://mlsanity.com/paper/1703.04247/) (2017)
- [Multimodal Transformer for Unaligned Multimodal Language Sequences](https://mlsanity.com/paper/1906.00295/) (2019)
- [AutoInt: Automatic Feature Interaction Learning via Self-Attentive Neural Networks](https://mlsanity.com/paper/1810.11921/) (2018)
- [Open Benchmarking for Click-Through Rate Prediction](https://mlsanity.com/paper/2009.05794/) (2020)
- [AutoFIS: Automatic Feature Interaction Selection in Factorization Models for Click-Through Rate Prediction](https://mlsanity.com/paper/2003.11235/) (2020)
- [Knowing your FATE: Friendship, Action and Temporal Explanations for User Engagement Prediction on Social Apps](https://mlsanity.com/paper/2006.06427/) (2020)
- [GroupIM: A Mutual Information Maximization Framework for Neural Group Recommendation](https://mlsanity.com/paper/2006.03736/) (2020)
- [Multimodality Invariant Learning for Multimedia-Based New Item Recommendation](https://mlsanity.com/paper/2405.15783/) (2024)
- [Inf-VAE: A Variational Autoencoder Framework to Integrate Homophily and Influence in Diffusion Prediction](https://mlsanity.com/paper/2001.00132/) (2020)
- [Dense Multimodal Fusion for Hierarchically Joint Representation](https://mlsanity.com/paper/1810.03414/) (2018)
- [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)
- [Distributed Algorithms for Fully Personalized PageRank on Large Graphs](https://mlsanity.com/paper/1903.11749/) (2019)
- [Capacity Constrained Influence Maximization in Social Networks](https://mlsanity.com/paper/2306.01782/) (2023)
- [Measuring Friendship Closeness: A Perspective of Social Identity Theory](https://mlsanity.com/paper/2208.09176/) (2022)
- [Finding Near-Optimal Maximum Set of Disjoint k-Cliques in Real-World Social Networks](https://mlsanity.com/paper/2503.20299/) (2025)

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

- [Heterogeneous Influence Maximization in User Recommendation](https://mlsanity.com/paper/2508.13517/) (2025)

## Abstract (arXiv)

Due to the convenience of mobile devices, the online games have become an important part for user entertainments in reality, creating a demand for friend recommendation in online games. However, none of existing approaches can effectively incorporate the multi-modal user features (e.g., images and texts) with the structural information in the friendship graph, due to the following limitations: (1) some of them ignore the high-order structural proximity between users, (2) some fail to learn the pairwise relevance between users at modality-specific level, and (3) some cannot capture both the local and global user preferences on different modalities. By addressing these issues, in this paper, we propose an end-to-end model FROG that better models the user preferences on potential friends. Comprehensive experiments on both offline evaluation and online deployment at Tencent have demonstrated the superiority of FROG over existing approaches.