User Behavior Analysis and Prediction Methods for Large-scale Video-on- demand System
Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Video-on-demand (VOD) systems are some of the best-known examples of 'next-generation' Internet applications. With their growing popularity, huge amount of video content imposes a heavy burden on Internet traffic which, in turns, influences the user experience of the systems. Predicting and pre- fetching relevant content before user requests is one of the popular methods used to reduce the start-up delay. In this paper, a typical VOD system is characterized and user's watching behavior is analyzed. Based on the characterization, two pre- fetching approaches based on user behavior are investigated. One is to prediction relevant content based on access history. The other is prediction based on user-clustering. The results clearly indicate the value of pre-fetching approaches for VOD systems and lead to the discussions on future work for further improvement.
Place, publisher, year, edition, pages
2015. , 51 p.
Engineering and Technology
IdentifiersURN: urn:nbn:se:uu:diva-263261OAI: oai:DiVA.org:uu-263261DiVA: diva2:857596
Master Programme in Human-Computer Interaction
Lind, MatsOestreicher, Lars