A Large-scale Friend Suggestion Architecture

TL;DR

This paper introduces a large-scale friend suggestion system for online game platforms that models user similarity evolution, effectively handling rapid preference changes and large user bases to improve recommendation accuracy.

Contribution

It proposes a novel friend ranking model that captures both long-term and short-term user features, addressing challenges unique to online gaming environments.

Findings

Model outperforms baseline methods on large-scale datasets

Effective in capturing dynamic user preferences

Scalable to millions of users

Abstract

Online social as an extension of traditional life plays an important role in our daily lives. Users often seek out new friends that have significant similarities such as interests and habits, motivating us to exploit such online information to suggest friends to users. In this work, we focus on friend suggestion in online game platforms because in-game social quality significantly correlates with player engagement, determining game experience. Unlike a typical recommendation system that depends on item-user interactions, in our setting, user-user interactions do not depend on each other. Meanwhile, user preferences change rapidly due to fast changing game environment. There has been little work on designing friend suggestion when facing these difficulties, and for the first time we aim to tackle this in large scale online games. Motivated by the fast changing online game environment, we formulate this problem as friend ranking by modeling the evolution of similarity among users, exploiting the long-term and short-term feature of users in games. Our experiments on large-scale game datasets with several million users demonstrate that our proposed model achieves superior performance over other competing baselines.