85 research outputs found
Difficulty Modelling in Mobile Puzzle Games: An Empirical Study on Different Methods to Combine Player Analytics and Simulated Data
Difficulty is one of the key drivers of player engagement and it is often one
of the aspects that designers tweak most to optimise the player experience;
operationalising it is, therefore, a crucial task for game development studios.
A common practice consists of creating metrics out of data collected by player
interactions with the content; however, this allows for estimation only after
the content is released and does not consider the characteristics of potential
future players.
In this article, we present a number of potential solutions for the
estimation of difficulty under such conditions, and we showcase the results of
a comparative study intended to understand which method and which types of data
perform better in different scenarios.
The results reveal that models trained on a combination of cohort statistics
and simulated data produce the most accurate estimations of difficulty in all
scenarios. Furthermore, among these models, artificial neural networks show the
most consistent results
Combining Sequential and Aggregated Data for Churn Prediction in Casual Freemium Games
In freemium games, the revenue from a player comes from the in-app purchases
made and the advertisement to which that player is exposed. The longer a player
is playing the game, the higher will be the chances that he or she will
generate a revenue within the game. Within this scenario, it is extremely
important to be able to detect promptly when a player is about to quit playing
(churn) in order to react and attempt to retain the player within the game,
thus prolonging his or her game lifetime. In this article we investigate how to
improve the current state-of-the-art in churn prediction by combining
sequential and aggregate data using different neural network architectures. The
results of the comparative analysis show that the combination of the two data
types grants an improvement in the prediction accuracy over predictors based on
either purely sequential or purely aggregated data
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