31,979 research outputs found
Customer churn prediction in telecom using machine learning and social network analysis in big data platform
Customer churn is a major problem and one of the most important concerns for
large companies. Due to the direct effect on the revenues of the companies,
especially in the telecom field, companies are seeking to develop means to
predict potential customer to churn. Therefore, finding factors that increase
customer churn is important to take necessary actions to reduce this churn. The
main contribution of our work is to develop a churn prediction model which
assists telecom operators to predict customers who are most likely subject to
churn. The model developed in this work uses machine learning techniques on big
data platform and builds a new way of features' engineering and selection. In
order to measure the performance of the model, the Area Under Curve (AUC)
standard measure is adopted, and the AUC value obtained is 93.3%. Another main
contribution is to use customer social network in the prediction model by
extracting Social Network Analysis (SNA) features. The use of SNA enhanced the
performance of the model from 84 to 93.3% against AUC standard. The model was
prepared and tested through Spark environment by working on a large dataset
created by transforming big raw data provided by SyriaTel telecom company. The
dataset contained all customers' information over 9 months, and was used to
train, test, and evaluate the system at SyriaTel. The model experimented four
algorithms: Decision Tree, Random Forest, Gradient Boosted Machine Tree "GBM"
and Extreme Gradient Boosting "XGBOOST". However, the best results were
obtained by applying XGBOOST algorithm. This algorithm was used for
classification in this churn predictive model.Comment: 24 pages, 14 figures. PDF https://rdcu.be/budK
Deep Learning based Recommender System: A Survey and New Perspectives
With the ever-growing volume of online information, recommender systems have
been an effective strategy to overcome such information overload. The utility
of recommender systems cannot be overstated, given its widespread adoption in
many web applications, along with its potential impact to ameliorate many
problems related to over-choice. In recent years, deep learning has garnered
considerable interest in many research fields such as computer vision and
natural language processing, owing not only to stellar performance but also the
attractive property of learning feature representations from scratch. The
influence of deep learning is also pervasive, recently demonstrating its
effectiveness when applied to information retrieval and recommender systems
research. Evidently, the field of deep learning in recommender system is
flourishing. This article aims to provide a comprehensive review of recent
research efforts on deep learning based recommender systems. More concretely,
we provide and devise a taxonomy of deep learning based recommendation models,
along with providing a comprehensive summary of the state-of-the-art. Finally,
we expand on current trends and provide new perspectives pertaining to this new
exciting development of the field.Comment: The paper has been accepted by ACM Computing Surveys.
https://doi.acm.org/10.1145/328502
- …