148 research outputs found
From Non-Paying to Premium: Predicting User Conversion in Video Games with Ensemble Learning
Retaining premium players is key to the success of free-to-play games, but
most of them do not start purchasing right after joining the game. By
exploiting the exceptionally rich datasets recorded by modern video
games--which provide information on the individual behavior of each and every
player--survival analysis techniques can be used to predict what players are
more likely to become paying (or even premium) users and when, both in terms of
time and game level, the conversion will take place. Here we show that a
traditional semi-parametric model (Cox regression), a random survival forest
(RSF) technique and a method based on conditional inference survival ensembles
all yield very promising results. However, the last approach has the advantage
of being able to correct the inherent bias in RSF models by dividing the
procedure into two steps: first selecting the best predictor to perform the
splitting and then the best split point for that covariate. The proposed
conditional inference survival ensembles method could be readily used in
operational environments for early identification of premium players and the
parts of the game that may prompt them to become paying users. Such knowledge
would allow developers to induce their conversion and, more generally, to
better understand the needs of their players and provide them with a
personalized experience, thereby increasing their engagement and paving the way
to higher monetization.Comment: social games, conversion prediction, ensemble methods, survival
analysis, online games, user behavio
Assembling real networks from synthetic and unstructured subsets: the corporate reporting case
The analysis of interfirm business transaction networks provides invaluable insight into the trading dynamics and economic structure of countries. However, there is a general scarcity of data available recording real, accurate and extensive information for these types of networks. As a result, and in common with other types of network studies - such as protein interactions for instance - research tends to rely on partial and incomplete datasets, i.e. subsets, with less certain conclusions. Hereh, we make use of unstructured financial and corporate reporting data in Japan as the base source to construct a financial reporting network, which is then compared and contrasted to the wider real business transaction network. The comparative analysis between these two rich datasets - the proxy, partially derived network and the real, complete network at macro as well as local structural levels - provides an enhanced understanding of the non trivial relationships between partial sampled subsets and fully formed networks. Furthermore, we present an elemental agent based pruning algorithm that reconciles and preserves key structural differences between these two networks, which may serve as an embryonic generic framework of potentially wider use to network research, enabling enhanced extrapolation of conclusions from partial data or subsets
Holographic Spacetimes as Quantum Circuits of Path-Integrations
We propose that holographic spacetimes can be regarded as collections of
quantum circuits based on path-integrals. We relate a codimension one surface
in a gravity dual to a quantum circuit given by a path-integration on that
surface with an appropriate UV cut off. Our proposal naturally generalizes the
conjectured duality between the AdS/CFT and tensor networks. This largely
strengthens the surface/state duality and also provides a holographic
explanation of path-integral optimizations. For static gravity duals, our new
framework provides a derivation of the holographic complexity formula given by
the gravity action on the WDW patch. We also propose a new formula which
relates numbers of quantum gates to surface areas, even including time-like
surfaces, as a generalization of the holographic entanglement entropy formula.
We argue the time component of the metric in AdS emerges from the density of
unitary quantum gates in the dual CFT. Our proposal also provides a heuristic
understanding how the gravitational force emerges from quantum circuits.Comment: 39 pages, 13 figures, latex; v2: appendix B added for an explicit
analysis of path-integral quantum circuits, counting scrambling quantum gates
clarified, references included; v3: a reference adde
Forecasting Player Behavioral Data and Simulating in-Game Events
Understanding player behavior is fundamental in game data science. Video
games evolve as players interact with the game, so being able to foresee player
experience would help to ensure a successful game development. In particular,
game developers need to evaluate beforehand the impact of in-game events.
Simulation optimization of these events is crucial to increase player
engagement and maximize monetization. We present an experimental analysis of
several methods to forecast game-related variables, with two main aims: to
obtain accurate predictions of in-app purchases and playtime in an operational
production environment, and to perform simulations of in-game events in order
to maximize sales and playtime. Our ultimate purpose is to take a step towards
the data-driven development of games. The results suggest that, even though the
performance of traditional approaches such as ARIMA is still better, the
outcomes of state-of-the-art techniques like deep learning are promising. Deep
learning comes up as a well-suited general model that could be used to forecast
a variety of time series with different dynamic behaviors
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