Behavior Structformer: Learning Players Representations with Structured Tokenization
Not on the map. Why?
Abstract (arXiv)
In this paper, we introduce the Behavior Structformer, a method for modeling user behavior using structured tokenization within a Transformer-based architecture. By converting tracking events into dense tokens, this approach enhances model training efficiency and effectiveness. We demonstrate its superior performance through ablation studies and benchmarking against traditional tabular and semi-structured baselines. The results indicate that structured tokenization with sequential processing significantly improves behavior modeling.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 9 of its 14 references that are in the map. First posted on arXiv 7 Jun 2024.
Builds on
9 of 14 references.
References · 9
- 2017Attention Is All You Needcited by 60,361
- 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
- 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 30,186
- 2019BERT4Rec: Sequential Recommendation with Bidirectional Encoder Representations from Transformercited by 964
- 2020Captum: A unified and generic model interpretability library for PyTorchcited by 547
- 2019BEHRT: Transformer for Electronic Health Recordscited by 263
- 2022Modeling Users' Behavior Sequences with Hierarchical Explainable Network for Cross-domain Fraud Detectioncited by 13
- 2018Sequential Behavioral Data Processing Using Deep Learning and the Markov Transition Field in Online Fraud Detectioncited by 5
- 2024player2vec: A Language Modeling Approach to Understand Player Behavior in Gamescited by 5
Built on by
Citing papers per year: 2026: 1.
1 paper.
- 2026AI for Games in the Foundation Model Eracited by 3
- 2026AI for Games in the Foundation Model Era15 Sep 2026