Summary of Player2vec: a Language Modeling Approach to Understand Player Behavior in Games, by Tianze Wang et al.
player2vec: A Language Modeling Approach to Understand Player Behavior in Games
by Tianze Wang, Maryam Honari-Jahromi, Styliani Katsarou, Olga Mikheeva, Theodoros Panagiotakopoulos, Sahar Asadi, Oleg Smirnov
First submitted to arxiv on: 5 Apr 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper proposes a novel method for learning latent user representations from historical behavior logs in video and mobile gaming contexts. This approach extends a long-range Transformer model from natural language processing to player behavior data, enabling self-supervised learning of player representations without ground-truth annotations. The proposed preprocessing and tokenization approaches view in-game events as analogous to words in sentences, allowing for the extraction of insights into behavior patterns that can inform downstream applications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper introduces a new method for understanding player behavior in video and mobile gaming by applying natural language processing techniques. It shows how to use a Transformer model to learn about players’ actions without needing labeled data. The approach helps identify patterns in game behavior, which can be used to make recommendations or improve the overall gaming experience. |
Keywords
* Artificial intelligence * Natural language processing * Self supervised * Tokenization * Transformer
