# DraftRec: Personalized Draft Recommendation for Winning in Multi-Player Online Battle Arena Games

arXiv: https://arxiv.org/abs/2204.12750 · HTML: https://mlsanity.com/paper/2204.12750/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 50 references that are in the map.
- First posted on arXiv 27 Apr 2022.

## Builds on (most cited first, 22 of 22)

- [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)
- [A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks](https://mlsanity.com/paper/1610.02136/) (2016)
- [Enhancing The Reliability of Out-of-distribution Image Detection in Neural Networks](https://mlsanity.com/paper/1706.02690/) (2017)
- [Off-Policy Deep Reinforcement Learning without Exploration](https://mlsanity.com/paper/1812.02900/) (2018)
- [Self-Attentive Sequential Recommendation](https://mlsanity.com/paper/1808.09781/) (2018)
- [Neural Collaborative Filtering](https://mlsanity.com/paper/1708.05031/) (2017)
- [Dota 2 with Large Scale Deep Reinforcement Learning](https://mlsanity.com/paper/1912.06680/) (2019)
- [BERT4Rec: Sequential Recommendation with Bidirectional Encoder Representations from Transformer](https://mlsanity.com/paper/1904.06690/) (2019)
- [Stabilizing Off-Policy Q-Learning via Bootstrapping Error Reduction](https://mlsanity.com/paper/1906.00949/) (2019)
- [Session-based Recommendations with Recurrent Neural Networks](https://mlsanity.com/paper/1511.06939/) (2015)
- [Behavior Regularized Offline Reinforcement Learning](https://mlsanity.com/paper/1911.11361/) (2019)
- [Recurrent Neural Networks with Top-k Gains for Session-based Recommendations](https://mlsanity.com/paper/1706.03847/) (2017)
- [Mastering Complex Control in MOBA Games with Deep Reinforcement Learning](https://mlsanity.com/paper/1912.09729/) (2019)
- [Empirical Study of Off-Policy Policy Evaluation for Reinforcement Learning](https://mlsanity.com/paper/1911.06854/) (2019)
- [Personalizing Session-based Recommendations with Hierarchical Recurrent Neural Networks](https://mlsanity.com/paper/1706.04148/) (2017)
- [Towards Playing Full MOBA Games with Deep Reinforcement Learning](https://mlsanity.com/paper/2011.12692/) (2020)
- [RL Unplugged: A Suite of Benchmarks for Offline Reinforcement Learning](https://mlsanity.com/paper/2006.13888/) (2020)
- [The Art of Drafting: A Team-Oriented Hero Recommendation System for Multiplayer Online Battle Arena Games](https://mlsanity.com/paper/1806.10130/) (2018)
- ["And then they died": Using Action Sequences for Data Driven,Context Aware Gameplay Analysis](https://mlsanity.com/paper/2006.10823/) (2020)
- [Interpretable Contextual Team-aware Item Recommendation: Application in Multiplayer Online Battle Arena Games](https://mlsanity.com/paper/2007.15236/) (2020)
- [Modeling Game Avatar Synergy and Opposition through Embedding in Multiplayer Online Battle Arena Games](https://mlsanity.com/paper/1803.10402/) (2018)

## Built on by (most cited first, 6 of 6)

- [ST-RAP: A Spatio-Temporal Framework for Real Estate Appraisal](https://mlsanity.com/paper/2308.10609/) (2023)
- [SC2EGSet: StarCraft II Esport Replay and Game-state Dataset](https://mlsanity.com/paper/2207.03428/) (2022)
- [CUPID: Improving Battle Fairness and Position Satisfaction in Online MOBA Games with a Re-matchmaking System](https://mlsanity.com/paper/2406.19720/) (2024)
- [Action2Score: An Embedding Approach To Score Player Action](https://mlsanity.com/paper/2207.10297/) (2022)
- [Voice Communication Analysis in Esports](https://mlsanity.com/paper/2411.19793/) (2024)
- [When to Switch, Not Just What: Transition Quality Prediction in Clash Royale](https://mlsanity.com/paper/2605.21868/) (2026)

## Abstract (arXiv)

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. Our code and dataset are available at https://github.com/dojeon-ai/DraftRec.