TY - GEN
T1 - Probabilistic seeking prediction in P2P VoD systems
AU - Wang, Weiwei
AU - Xu, Tianyin
AU - Gao, Yang
AU - Lu, Sanglu
PY - 2009
Y1 - 2009
N2 - 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, frequent sequential patterns mining is first performed to extract abstract states which are not overlapped and cover the whole video file altogether. After mapping the raw training 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.
AB - 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, frequent sequential patterns mining is first performed to extract abstract states which are not overlapped and cover the whole video file altogether. After mapping the raw training 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.
KW - Contingency table
KW - P2P VoD systems
KW - PrefixSpan
KW - State abstraction
KW - User behavior modeling
KW - User seeking prediction
UR - http://www.scopus.com/inward/record.url?scp=78650444031&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=78650444031&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-10439-8_68
DO - 10.1007/978-3-642-10439-8_68
M3 - Conference contribution
AN - SCOPUS:78650444031
SN - 364210438X
SN - 9783642104381
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 676
EP - 685
BT - AI 2009
T2 - 22nd Australasian Joint Conference on Artificial Intelligence, AI 2009
Y2 - 1 December 2009 through 1 December 2009
ER -