Shahrood University of Technology Journal of AI and Data Mining 2322-5211 Articles in Press 2026 06 06 DOTA-Draft: A Dataset for In-Game Recommendation in Multiplayer Online Battle Arenas 3801 10.22044/jadm.2026.16888.2819 EN Mohammadreza Mohammadnejad Electrical and Computer Engineering Department, Semnan University, Semnan, Iran. Morteza Dorrigiv Semnan University Farzin Yaghmaee Electrical and Computer Engineering Department, Semnan University, Semnan, Iran. Journal Article 2025 10 04 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.