The world of mobile gaming is persistently evolving, with a continually intense focus on creating personalized and engaging experiences. Traditional methodologies to decipher player behaviour have become grossly inadequate due to the rapidly paced, dynamic nature of gaming. Researchers from KTH Royal Institute of Technology, Sweden, have proposed an innovative solution.
A paper released by the institute sheds light on the unique methodology of ‘player2vec’. Primarily a language modeling technique, it incorporates features of self-supervised learning and Transformer-based architectures, which have been designed for Natural Language Processing, and applies them to mobile gaming.
Several techniques to model player behaviour have been employed, like collaborative filtering, neural networks and Markov models. However, these have limitations as they don’t fully capture the myriad complexities of mobile gaming. Player2vec adopts techniques from Natural Language Processing, visualising player interactions similarly to sentences in a language. The analogy helps unravel intricate patterns of gaming behaviour.
The blueprint of player2vec lies in the pre-processing stage. It transforms raw gaming session data into textual sequences. These sequences inspire natural language processing techniques and are fed into a Longformer model, a derivative of Transformer architecture. This model exhibits a special capacity for processing long sequences. During this stage, the model develops a skill to generate representations of player behaviour that are abundant in context. The pathway to several applications becomes clearer, notably in player segmentation and personalisation.
The utility of this approach surpasses representation learning. Besides, a qualitative analysis of the embedding space learnt shows distinct player clusters. These clusters provide profound insights into the various styles and motivations of the varied gaming community.
In addition, an experimental evaluation of the methodology showcases the ability of the player2vec approach to accurately model player event distribution, highlighted by strong performance on intrinsic language modelling metrics. This provides a reliable tool for personalised recommendations, gaming design optimisation, and targeted marketing campaigns.
This innovative methodology promises a potential revolution in our comprehension of player behaviour during gaming. It decodes complex patterns of player-game interaction by using language modelling and self-supervised learning. Player2vec holds the promise of refining the gaming experience, aiding gaming design decisions, and unveiling new horizons in the field of mobile gaming. All research credit goes to KTH Royal Institute of Technology and their research team.
