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Data Mining for Item Recommendation in MOBA Games

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Data Mining for Item Recommendation in MOBA Games

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This repository contains the implementation of the Recommender System Based on Classifiers (Section 4.3). The models are based on neural networks, logistic regression, and decision trees.

Results

Original result from our paper:

Method Precision Recall F1-Score MAP MRR
D Tree@1 0.65 0.17 0.27 0.64 0.64
Logit@1 0.67 0.18 0.28 0.67 0.67
ANN@1 0.71 0.19 0.30 0.71 0.71
D Tree@3 0.47 0.37 0.41 0.71 0.73
Logit@3 0.53 0.42 0.46 0.74 0.76
ANN@3 0.60 0.48 0.52 0.78 0.79
D Tree@6 0.32 0.50 0.38 0.69 0.75
Logit@6 0.37 0.59 0.43 0.71 0.78
ANN@6 0.44 0.69 0.53 0.74 0.81

Citation

If you find this repository useful for your research, please consider citing our paper:

@inproceedings{10.1145/3298689.3346986,
	author = {Araujo, Vladimir and Rios, Felipe and Parra, Denis},
	title = {Data Mining for Item Recommendation in MOBA Games},
	year = {2019},
	isbn = {9781450362436},
	publisher = {Association for Computing Machinery},
	address = {New York, NY, USA},
	url = {https://doi.org/10.1145/3298689.3346986},
	doi = {10.1145/3298689.3346986},
	booktitle = {Proceedings of the 13th ACM Conference on Recommender Systems},
	pages = {393–397},
	numpages = {5},
	keywords = {item recommendation, MOBA games, data mining},
	location = {Copenhagen, Denmark},
	series = {RecSys ’19}
}

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