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Non-Contractual Churn Prediction with Limited User Information
KTH, School of Engineering Sciences (SCI), Mathematics (Dept.), Mathematical Statistics.
2019 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesisAlternative title
Förutsägning av avtalslöst tittaravhopp med begränsad användarinformation (Swedish)
Abstract [en]

This report compares the effectiveness of three statistical methods for predicting defecting viewers in SVT's video on demand (VOD) services: logistic regression, random forests, and long short-term memory recurrent neural networks (LSTMs). In particular, the report investigates whether or not sequential data consisting of users' weekly watch histories can be used with LSTMs to achieve better predictive performance than the two other methods. The study found that the best LSTM models did outperform the other methods in terms of precision, recall, F-measure and AUC – but not accuracy. Logistic regression and random forests offered comparable performance results. The models are however subject to several notable limitations, so further research is advised.

Abstract [sv]

Den här rapporten undersöker effektiviteten av tre statistiska metoder för att förutse tittaravhopp i SVT:s playtjänster: logistisk regression, random forests och rekurrenta neurala nätverk av varianten long short-term memory (LSTM:s). I synnerhet försöker studien utröna huruvida sekventiell data i form av tittares veckovisa besökshistorik kan användas med LSTM:s för att nå bättre prediktionsprestanda än de övriga två metoderna. Studien fann att LSTM-modeller genererade bättre precision, täckning, F-mått och AUC – men inte träffsäkerhet. Prestandan av logistisk regression och random forests visade sig vara jämförbara. På grund av modellernas många begränsningar finns det dock gott om utrymme för vidare forskning och utveckling.

Place, publisher, year, edition, pages
2019.
Series
TRITA-SCI-GRU ; 2019:08
National Category
Probability Theory and Statistics
Identifiers
URN: urn:nbn:se:kth:diva-252343OAI: oai:DiVA.org:kth-252343DiVA, id: diva2:1320100
External cooperation
SVT AB
Subject / course
Mathematical Statistics
Educational program
Master of Science - Applied and Computational Mathematics
Supervisors
Examiners
Available from: 2019-06-04 Created: 2019-06-04 Last updated: 2019-06-11Bibliographically approved

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CiteExportLink to record
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