16 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
Modeling Attrition in Organizations from Email Communication
Abstract—Modeling people’s online behavior in relation to their real-world social context is an interesting and important research problem. In this paper, we present our preliminary study of attrition behavior in real-world organizations based on two online datasets: a dataset from a small startup (40+ users) and a dataset from one large US company (3600+ users). The small startup dataset is collected using our privacy-preserving data logging tool, which removes personal identifiable information from content data and extracts only aggregated statistics such as word frequency counts and sentiment features. The privacy-preserving measures have enabled us to recruit participants to support this study. Correlation analysis over the startup dataset has shown that statistically there is often a change point in people’s online behavior, and data exhibits weak trends that may be manifestation of real-world attrition. Same findings are also verified in the large company dataset. Furthermore, we have trained a classifier to predict real-world attrition with a moderate accuracy of 60-65 % on the large company dataset. Given the incompleteness and noisy nature of data, the accuracy is encouraging. I
Enhancing Person-Job Fit for Talent Recruitment: An Ability-aware Neural Network Approach
The wide spread use of online recruitment services has led to information
explosion in the job market. As a result, the recruiters have to seek the
intelligent ways for Person Job Fit, which is the bridge for adapting the right
job seekers to the right positions. Existing studies on Person Job Fit have a
focus on measuring the matching degree between the talent qualification and the
job requirements mainly based on the manual inspection of human resource
experts despite of the subjective, incomplete, and inefficient nature of the
human judgement. To this end, in this paper, we propose a novel end to end
Ability aware Person Job Fit Neural Network model, which has a goal of reducing
the dependence on manual labour and can provide better interpretation about the
fitting results. The key idea is to exploit the rich information available at
abundant historical job application data. Specifically, we propose a word level
semantic representation for both job requirements and job seekers' experiences
based on Recurrent Neural Network. Along this line, four hierarchical ability
aware attention strategies are designed to measure the different importance of
job requirements for semantic representation, as well as measuring the
different contribution of each job experience to a specific ability
requirement. Finally, extensive experiments on a large scale real world data
set clearly validate the effectiveness and interpretability of the APJFNN
framework compared with several baselines.Comment: This is an extended version of our SIGIR18 pape
On Statistical Discrimination as a Failure of Social Learning: A Multi-Armed Bandit Approach
We analyze statistical discrimination in hiring markets using a multi-armed
bandit model. Myopic firms face workers arriving with heterogeneous observable
characteristics. The association between the worker's skill and characteristics
is unknown ex ante; thus, firms need to learn it. Laissez-faire causes
perpetual underestimation: minority workers are rarely hired, and therefore,
underestimation towards them tends to persist. Even a slight population-ratio
imbalance frequently produces perpetual underestimation. We propose two policy
solutions: a novel subsidy rule (the hybrid mechanism) and the Rooney Rule. Our
results indicate that temporary affirmative actions effectively mitigate
discrimination caused by insufficient data
Modeling sequential preferences with dynamic user and context factors
National Research Foundation (NRF) Singapor
JobComposer: Career path optimization via multicriteria utility learning
With online professional network platforms (OPNs, e.g., LinkedIn, Xing, etc.)
becoming popular on the web, people are now turning to these platforms to
create and share their professional profiles, to connect with others who share
similar professional aspirations and to explore new career opportunities. These
platforms however do not offer a long-term roadmap to guide career progression
and improve workforce employability. The career trajectories of OPN users can
serve as a reference but they are not always optimal. A career plan can also be
devised through consultation with career coaches, whose knowledge may however
be limited to a few industries. To address the above limitations, we present a
novel data-driven approach dubbed JobComposer to automate career path planning
and optimization. Its key premise is that the observed career trajectories in
OPNs may not necessarily be optimal, and can be improved by learning to
maximize the sum of payoffs attainable by following a career path. At its
heart, JobComposer features a decomposition-based multicriteria utility
learning procedure to achieve the best tradeoff among different payoff criteria
in career path planning. Extensive studies using a city state-based OPN dataset
demonstrate that JobComposer returns career paths better than other baseline
methods and the actual career paths