Probabilistic Seeking Prediction in P2P VoD Systems

In P2P VoD streaming systems, user behavior modeling is critical to help optimise user experience as well as system throughput. However, it still remains a challenging task due to the dynamic characteristics of user viewing behavior. In this paper, we consider the problem of user seeking prediction which is to predict the user’s next seeking position so that the system can proactively make response. We present a novel method for solving this problem. In our method, frequential patterns mining is first performed to extract abstract states which are not overlapped and cover the whole video file altogether. After mapping the ning dataset to state transitions according to the abstract states, we use a simpel probabilistic contingency table to build the prediction model. We design an experiment on the synthetic P2P VoD dataset. The results demonstrate the effectiveness of our method.

• Type: Conference
• Year: 2009
• Where: AUSAI
• Authors: Weiwei Wang, Tianyin Xu, Yang Gao, Sanglu Lu
• Keywords: Artificial Intelligence, AUSAI 2009, P2P VoD, User, User Seeking Prediction