TY - GEN
T1 - Indie Games Popularity Prediction by Considering Multimodal Features
AU - Huang, Yu Heng
AU - Chu, Wei Ta
N1 - Funding Information:
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.
Publisher Copyright:
© 2022, Springer Nature Switzerland AG.
PY - 2022
Y1 - 2022
N2 - 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.
AB - 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.
UR - https://www.scopus.com/pages/publications/85127200383
UR - https://www.scopus.com/pages/publications/85127200383#tab=citedBy
U2 - 10.1007/978-3-030-98355-0_5
DO - 10.1007/978-3-030-98355-0_5
M3 - Conference contribution
AN - SCOPUS:85127200383
SN - 9783030983543
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 52
EP - 61
BT - MultiMedia Modeling - 28th International Conference, MMM 2022, Proceedings
A2 - Þór Jónsson, Björn
A2 - Gurrin, Cathal
A2 - Tran, Minh-Triet
A2 - Dang-Nguyen, Duc-Tien
A2 - Hu, Anita Min-Chun
A2 - Huynh Thi Thanh, Binh
A2 - Huet, Benoit
PB - Springer Science and Business Media Deutschland GmbH
T2 - 28th International Conference on MultiMedia Modeling, MMM 2022
Y2 - 6 June 2022 through 10 June 2022
ER -