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Towards Virtual 3D Asset Price Prediction Based on Machine Learning
| Content Provider | MDPI |
|---|---|
| Author | Korbel, Jakob J. Siddiq, Umar H. Zarnekow, Rüdiger |
| Copyright Year | 2022 |
| Description | Although 3D models are today indispensable in various industries, the adequate pricing of 3D models traded on online platforms, i.e., virtual 3D assets, remains vague. This study identifies relevant price determinants of virtual 3D assets through the analysis of a dataset containing the characteristics of 135.384 3D models. Machine learning algorithms were applied to derive a virtual 3D asset price prediction tool based on the analysis results. The evaluation revealed that the random forest regression model is the most promising model to predict virtual 3D asset prices. Furthermore, the findings imply that the geometry and number of material files, as well as the quality of textures, are the most relevant price determinants, whereas animations and file formats play a minor role. However, the analysis also showed that the pricing behavior is still substantially influenced by the subjective assessment of virtual 3D asset creators. |
| Ending Page | 948 |
| Page Count | 25 |
| Starting Page | 924 |
| e-ISSN | 07181876 |
| DOI | 10.3390/jtaer17030048 |
| Journal | Journal of theoretical and applied electronic commerce research |
| Issue Number | 3 |
| Volume Number | 17 |
| Language | English |
| Publisher | MDPI |
| Publisher Date | 2022-07-07 |
| Access Restriction | Open |
| Subject Keyword | Journal of theoretical and applied electronic commerce research Journal of Theoretical and Applied Electronic Commerce Research Information and Library Science 3d Model Virtual Asset Virtual Product Virtual Good Pricing Machine Learning Feature Scoring E-commerce Metaverse |
| Content Type | Text |
| Resource Type | Article |
