DraftRec: Personalized Draft Recommendation for Winning in Multi-Player Online Battle Arena Games (2022-04-25T00:00:00.000000Z)

TL;DR

A novel hierarchical model which recommends characters by considering each player’s champion preferences and the interaction between the players is proposed, which achieved state-of-the-art performance in character recommendation and match outcome prediction task.

Abstract

This paper presents a personalized character recommendation system for Multiplayer Online Battle Arena (MOBA) games which are considered as one of the most popular online video game genres around the world. When playing MOBA games, players go through a draft stage, where they alternately select a virtual character to play. When drafting, players select characters by not only considering their character preferences, but also the synergy and competence of their team’s character combination. However, the complexity of drafting induces difficulties for beginners to choose the appropriate characters based on the characters of their team while considering their own champion preferences. To alleviate this problem, we propose DraftRec, a novel hierarchical model which recommends characters by considering each player’s champion preferences and the interaction between the players. DraftRec consists of two networks: the player network and the match network. The player network captures the individual player’s champion preference, and the match network integrates the complex relationship between the players and their respective champions. We train and evaluate our model from a manually collected 280,000 matches of League of Legends and a publicly available 50,000 matches of Dota2. Empirically, our method achieved state-of-the-art performance in character recommendation and match outcome prediction task. Furthermore, a comprehensive user survey confirms that DraftRec provides convincing and satisfying recommendations to the real-world players. Our code and dataset are available at https://github.com/dojeon-ai/DraftRec .

Authors

Hojoon Lee

3 papers

J. Choo

11 papers

Dongyoon Hwang

1 papers

TL;DR

Abstract

Authors

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