Abstract
We present a popularity prediction system for independent computer games (indie games), by jointly considering visual, text, and metadata information. An indie game dataset is first collected and labeled. According to the number of sales, we label an indie game as popular or not. Different types of information is extracted by specific feature extractors, and then is fused to construct a neural network-based classifier. We demonstrate that jointly considering multimodal information yields promising performance. In addition, we show that, with helps of state-of-the-art feature embeddings, the proposed method outperforms the only existing SVM-based method.
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Acknowledgement
This work was funded in part by Qualcomm through a Taiwan University Research Collaboration Project and in part by the Ministry of Science and Technology, Taiwan, under grants 110-2221-E-006-127-MY3, 108-2221-E-006-227-MY3, 107-2923-E-006-009-MY3, and 109-2218-E-002-015.
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Huang, YH., Chu, WT. (2022). Indie Games Popularity Prediction by Considering Multimodal Features. In: Þór Jónsson, B., et al. MultiMedia Modeling. MMM 2022. Lecture Notes in Computer Science, vol 13142. Springer, Cham. https://doi.org/10.1007/978-3-030-98355-0_5
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DOI: https://doi.org/10.1007/978-3-030-98355-0_5
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