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short-paper

Data mining for item recommendation in MOBA games

Authors:

Vladimir Araujo,

Denis ParraAuthors Info & Claims

RecSys '19: Proceedings of the 13th ACM Conference on Recommender Systems

Pages 393 - 397

https://doi.org/10.1145/3298689.3346986

Published: 10 September 2019 Publication History

Abstract

E-Sports has been positioned as an important activity within MOBA (Multiplayer Online Battle Arena) games in recent years. There is existing research on recommender systems in this topic, but most of it focuses on the character recommendation problem. However, the recommendation of items is also challenging because of its contextual nature, depending on the other characters. We have developed a framework that suggests items for a character based on the match context. The system aims to help players who have recently started the game as well as frequent players to take strategic advantage during a match and to improve their purchasing decision making. By analyzing a dataset of ranked matches through data mining techniques, we can capture purchase dynamic of experienced players to use it to generate recommendations. The results show that our proposed solution yields up to 80% of mAP, suggesting that the method leverages context information successfully. These results, together with open issues we mention in the paper, call for further research in the area.

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Cited By

Wang LDormann CWallner G(2024)A Feature Comparison Study of Live Companion Tools for Esports GamesProceedings of the 19th International Conference on the Foundations of Digital Games10.1145/3649921.3650004(1-11)Online publication date: 21-May-2024
https://dl.acm.org/doi/10.1145/3649921.3650004
Costa LDrachen ASouza FXexéo G(2024)Artificial Intelligence in MOBA Games: A Multivocal Literature MappingIEEE Transactions on Games10.1109/TG.2023.328215716:2(250-269)Online publication date: Jun-2024
https://doi.org/10.1109/TG.2023.3282157
Bao ZSun XZhang W(2023)A Pre-game Item Recommendation Method Based on Self-Supervised Learning2023 IEEE 6th International Conference on Pattern Recognition and Artificial Intelligence (PRAI)10.1109/PRAI59366.2023.10331965(961-966)Online publication date: 18-Aug-2023
https://doi.org/10.1109/PRAI59366.2023.10331965
Show More Cited By

Index Terms

Data mining for item recommendation in MOBA games
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Recommender systems
  2. Information systems applications
    1. Data mining

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Information & Contributors

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Published In

cover image ACM Other conferences

RecSys '19: Proceedings of the 13th ACM Conference on Recommender Systems

September 2019

635 pages

ISBN:9781450362436

DOI:10.1145/3298689

General Chairs:
Toine Bogers
Aalborg University Copenhagen, Denmark
,
Alan Said
University of Gothenburg, Sweden
,
Program Chairs:
Peter Brusilovsky
University of Pittsburgh
,
Domonkos Tikk
Gravity R&D, Hungary

Copyright © 2019 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 10 September 2019

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Short-paper

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Millennium Institute for Foundational Research on Data (IMFD)

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RecSys '19

RecSys '19: Thirteenth ACM Conference on Recommender Systems

September 16 - 20, 2019

Copenhagen, Denmark

Acceptance Rates

RecSys '19 Paper Acceptance Rate 36 of 189 submissions, 19%;

Overall Acceptance Rate 254 of 1,295 submissions, 20%

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Cited By

Wang LDormann CWallner G(2024)A Feature Comparison Study of Live Companion Tools for Esports GamesProceedings of the 19th International Conference on the Foundations of Digital Games10.1145/3649921.3650004(1-11)Online publication date: 21-May-2024
https://dl.acm.org/doi/10.1145/3649921.3650004
Costa LDrachen ASouza FXexéo G(2024)Artificial Intelligence in MOBA Games: A Multivocal Literature MappingIEEE Transactions on Games10.1109/TG.2023.328215716:2(250-269)Online publication date: Jun-2024
https://doi.org/10.1109/TG.2023.3282157
Bao ZSun XZhang W(2023)A Pre-game Item Recommendation Method Based on Self-Supervised Learning2023 IEEE 6th International Conference on Pattern Recognition and Artificial Intelligence (PRAI)10.1109/PRAI59366.2023.10331965(961-966)Online publication date: 18-Aug-2023
https://doi.org/10.1109/PRAI59366.2023.10331965
Alfiani WNugroho FBasid PBahtiar FKurniawan FAdisusilo AArif Y(2023)Recommender Selection System for the Game Using Bonferroni Mean Based TOPSIS2023 8th International Conference on Electrical, Electronics and Information Engineering (ICEEIE)10.1109/ICEEIE59078.2023.10334773(1-6)Online publication date: 28-Sep-2023
https://doi.org/10.1109/ICEEIE59078.2023.10334773
Birant KBirant D(2022)Multi-Objective Multi-Instance Learning: A New Approach to Machine Learning for eSportsEntropy10.3390/e2501002825:1(28)Online publication date: 23-Dec-2022
https://doi.org/10.3390/e25010028
Rijnders FWallner GBernhaupt R(2022)Live Feedback for Training Through Real-Time Data Visualizations: A Study with League of LegendsProceedings of the ACM on Human-Computer Interaction10.1145/35495066:CHI PLAY(1-23)Online publication date: 31-Oct-2022
https://dl.acm.org/doi/10.1145/3549506
Araujo VSalinas HLabarca AVilla AParra DBellogin ABoratto LSantos OArdissono LKnijnenburg B(2022)Hierarchical Transformers for Group-Aware Sequential Recommendation: Application in MOBA GamesAdjunct Proceedings of the 30th ACM Conference on User Modeling, Adaptation and Personalization10.1145/3511047.3537667(293-301)Online publication date: 4-Jul-2022
https://dl.acm.org/doi/10.1145/3511047.3537667
Yue HLiu HChen J(2022) A Gospel for MOBA Game: Ranking-Preserved Hero Change Prediction in Dota 2 IEEE Transactions on Games10.1109/TG.2021.312358314:2(191-201)Online publication date: Jun-2022
https://doi.org/10.1109/TG.2021.3123583
Wong NKwok STan J(2022)Predictive Analytics of First Blood and Match Outcome in Dota 2TENCON 2022 - 2022 IEEE Region 10 Conference (TENCON)10.1109/TENCON55691.2022.9977786(1-6)Online publication date: 1-Nov-2022
https://doi.org/10.1109/TENCON55691.2022.9977786
Duan LLi SZhang WWang W(2022)MOBA Game Item Recommendation via Relation-aware Graph Attention Network2022 IEEE Conference on Games (CoG)10.1109/CoG51982.2022.9893595(338-344)Online publication date: 21-Aug-2022
https://doi.org/10.1109/CoG51982.2022.9893595
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