Document Type : Technical Paper

Authors

• Mohammadreza Mohammadnejad ¹
• Morteza Dorrigiv ²
• Farzin Yaghmaee ¹

¹ Electrical and Computer Engineering Department, Semnan University, Semnan, Iran.

² Semnan University

Abstract

Research in recommender systems has largely relied on standardized datasets such as MovieLens, Amazon Reviews, and Last.fm. However, these datasets are unsuitable for in-game recommendations, particularly in Multiplayer Online Battle Arenas (MOBAs), due to the sequential, team-based, and adversarial nature of gameplay. To identify essential characteristics for in-game recommendation datasets, we perform a cross-domain analysis of widely used recommendation datasets, evaluating their structural and distributional properties, including interaction space, matrix shape, sparsity, and Gini-based feature–shape diversity. Building on these insights, we curate DOTA-Draft, a research-ready dataset from raw professional Dota 2 matches, encoding sequential pick/ban states, patch versions, and match outcomes. Using this dataset, we conduct top-k drafting recommendation tasks and provide baseline results with Bayesian Personalized Ranking (BPR) and GRU4Rec. To facilitate adoption, DOTA-Draft is packaged in a RecBole-compatible format. This work establishes principled benchmarks for in-game recommendation, demonstrates the inadequacy of traditional user–item paradigms in dynamic, adversarial environments, and provides a foundation for developing models that account for sequential, multi-agent decision-making.

Keywords

• In-game recommendation systems
• Multiplayer Online Battle Arenas (MOBA)
• Dota 2 Interaction Dataset
• Session-based recommendation models
• Benchmarking with RecBole framework

Main Subjects

• H.3.2.6. Games and infotainment