Name
Abstract | ||
|---|---|---|
Nowadays, video games play a very important role in human life and no longer purely associated with escapism or entertainment. In fact, gaming has become an essential part of our daily routines, which give rise to the exponential growth of various online game platforms. By participating in such platforms, individuals generate a multitude of game data points, which, for example, can be further used for automatic user profiling and recommendation applications. However, the literature on automatic learning from the game data is relatively sparse, which had inspired us to tackle the problem of player profiling in this first preliminary study. Specifically, in this work, we approach the task of player gender prediction based on various types of game data. Our initial experimental results inspire further research on user profiling in the game domain. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1609/aaai.v33i01.330110025 | AAAI |
Field | DocType | Volume |
Profiling (computer programming),Computer science,Human–computer interaction,Artificial intelligence,Machine learning | Conference | 33 |
Citations | PageRank | References |
1 | 0.35 | 0 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ivan Samborskii | 1 | 27 | 2.25 |
| Aleksandr Farseev | 2 | 92 | 7.48 |
| Andrey Filchenkov | 3 | 46 | 15.80 |
| Tat-Seng Chua | 4 | 11749 | 653.09 |