TL;DR
DraftRec is a hierarchical recommendation system that personalizes character picks in MOBA games by modeling individual preferences and team interactions, improving draft quality and match outcomes.
Contribution
It introduces a novel hierarchical model with player and match networks for personalized character recommendation in MOBA games, trained on large-scale match data.
Findings
Achieved state-of-the-art performance in character recommendation.
Improved match outcome prediction accuracy.
User survey confirms recommendation satisfaction.
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…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Code & Models
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
