Data mining for item recommendation in MOBA games

Araujo, Vladimir; Rios, Felipe; Parra, Denis

doi:10.1145/3298689.3346986

Cited by 17 publications

(9 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We trained the model using Adam optimizer with a learning rate of 3e-4 until convergence. For evaluation, we compare our model with decision tree (D-Tree), logistic regression (Logit) and shallow artificial neural network (ANN) baselines of [3]. We also implemented an additional baseline based on Convolutional neural networks (CNN) for a stronger comparison.…”

Section: Methodsmentioning

confidence: 99%

“…However, there has been little work on item recommendation, recently showing two approaches based on data mining methods. One for the recommendation of the future item given an initial set of items [14] and another for the recommendation of a fixed item set [3]. We closely follow the methodology from [3]; however, unlike their approach that uses only a few attributes of the data, we leverage meaningful contextual information about the game such as the allies, enemies, and the role of champions.…”

Section: Related Workmentioning

confidence: 99%

“…Like [3], the model was supervised only with the items selected by each champion of the winning team. This way the likelihood of recommending the best items is maximised in order to win.…”

Section: Model Architecturementioning

confidence: 99%

See 2 more Smart Citations

Interpretable Contextual Team-aware Item Recommendation: Application in Multiplayer Online Battle Arena Games

Villa

Araujo

Cattan

et al. 2020

Fourteenth ACM Conference on Recommender Systems

Self Cite

View full text Add to dashboard Cite

The video game industry has adopted recommendation systems to boost users interest with a focus on game sales. Other exciting applications within video games are those that help the player make decisions that would maximize their playing experience, which is a desirable feature in real-time strategy video games such as Multiplayer Online Battle Arena (MOBA) like as DotA and LoL. Among these tasks, the recommendation of items is challenging, given both the contextual nature of the game and how it exposes the dependence on the formation of each team. Existing works on this topic do not take advantage of all the available contextual match data and dismiss potentially valuable information. To address this problem we develop TTIR, a contextual recommender model derived from the Transformer neural architecture that suggests a set of items to every team member, based on the contexts of teams and roles that describe the match. TTIR outperforms several approaches and provides interpretable recommendations through visualization of attention weights. Our evaluation indicates that both the Transformer architecture and the contextual information are essential to get the best results for this item recommendation task. Furthermore, a preliminary user survey indicates the usefulness of attention weights for explaining recommendations as well as ideas for future work. The code and dataset are available at: https://github.com/ojedaf/IC-TIR-Lol.

show abstract

Section: Methodsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Interpretable Contextual Team-aware Item Recommendation: Application in Multiplayer Online Battle Arena Games

Villa

Araujo

Cattan

et al. 2020

Fourteenth ACM Conference on Recommender Systems

Self Cite

View full text Add to dashboard Cite

show abstract

“…2) Players' experience; the main means are data resetting of training match, accuracy of skill release and damage caused in group battle, hot spots of hero's moving position, vision inserting and arranging position, ban-pick order and winning rate, real-time items recommendation system, dynamic winner prediction system. Gradually promote such professional data statistical analysis to the product side and provide it to MOBA game's public players [22].…”

Section: Data Mining On Moba Gamesmentioning

confidence: 99%

Machine Learning and Data mining on the innovation of E-sports industry

Chen

Mei

Huang

2020

International Journal of Education and Information Technologies

View full text Add to dashboard Cite

AI technology brings many revolutionary innovation opportunities to the e-sports industry. With the help of data mining, we can analyze the advantages and disadvantages of competitors, and predict the trend of the situation in the future. With the help of the agent created by intensive in-depth learning, it can assist players of different levels to carry out routine training, so as to improve the overall activity of the game. With the help of AI's big data advantage, AI can assist E-sports teaching to regard E-sports specialty as an experimental platform for using cutting-edge technology to reform and innovate traditional education and provide forward-looking guidance for future education. This paper uses CNN, LSTM, and LSTM + CNN three model to predict the outcome of the game according to the heroes selected by both teams, and has achieved good prediction results.

show abstract

“…The experimental results show that the proposed MVRRF method outperforms the RF method in terms of classification accuracy. We also compared our results with the results presented in the state-of-art studies (Araujo et al, 2019;Cardoso, 2019;Chandrasekaran et al, 2020;Villa et al, 2020) on the same dataset. According to the results, the proposed method achieved higher performance than the rest.…”

mentioning

confidence: 94%

Multi‐view rank‐based random forest: A new algorithm for prediction in eSports

Birant

2021

Expert Systems

View full text Add to dashboard Cite

The main problem associated with the random forest (RF) algorithm is its application of random feature subset selection technique over a single vector. In this technique, the irrelevant or redundant features in a single-view data have equal chances with the important features to be selected for training a classifier, leading to misclassification. To overcome this problem, this article proposes a novel algorithm, called multi-view rank-based random forest (MVRRF). The proposed algorithm builds a set of decision trees from multi-view data by using a rank-based feature selection strategy. The main advantages of our method are that (i) it extends the RF algorithm for multi-view learning, and (ii) it reduces the chances of irrelevant and redundant feature selection, and thus it usually improves the accuracy, generalizability and robustness of the classification models. The aim of our study is to predict the match result of the game League of Legends in electronic sports (eSports). Thus, the eSports teams can define trustworthy strategies through important features. The proposed method can be successfully used in other fields as well as eSports. The experiments that were conducted on a publicly available eSports dataset show that the proposed MVRRF algorithm (93.32%) outperforms the standard RF algorithm (86.38%) on multi-view data in terms of accuracy. Furthermore, the experimental results also show that our method achieved higher performance than the methods tested in the state of the art studies on the same dataset.

show abstract

Data mining for item recommendation in MOBA games

Cited by 17 publications

References 9 publications

Interpretable Contextual Team-aware Item Recommendation: Application in Multiplayer Online Battle Arena Games

Interpretable Contextual Team-aware Item Recommendation: Application in Multiplayer Online Battle Arena Games

Machine Learning and Data mining on the innovation of E-sports industry

Multi‐view rank‐based random forest: A new algorithm for prediction in eSports

Contact Info

Product

Resources

About