TY - JOUR
T1 - A hybrid Similarity measure of contents for TV personalization
AU - Yu, Zhiwen
AU - Zhou, Xingshe
AU - Zhou, Liang
AU - Du, Kejun
PY - 2010/8
Y1 - 2010/8
N2 - Similarity measure of contents plays an important role in TV personalization, e.g., TV content group recommendation and similar TV content retrieval, which essentially are content clustering and example-based retrieval. We define similar TV contents to be those with similar semantic information, e.g., plot, background, genre, etc. Several similarity measure methods, notably vector space model based and category hierarchy model based similarity measure schemes, have been proposed for the purpose of data clustering and example-based retrieval. Each method has advantages and shortcomings of its own in TV content similarity measure. In this paper, we propose a hybrid approach for TV content similarity measure, which combines both vector space model and category hierarchy model. The hybrid measure proposed here makes the most of TV metadata information and takes advantage of the two similarity measurements. It measures TV content similarity from the semantic level other than the physical level. Furthermore, we propose an adaptive strategy for setting the combination parameters. The experimental results showed that using the hybrid similarity measure proposed here is superior to using either alone for TV content clustering and example-based retrieval.
AB - Similarity measure of contents plays an important role in TV personalization, e.g., TV content group recommendation and similar TV content retrieval, which essentially are content clustering and example-based retrieval. We define similar TV contents to be those with similar semantic information, e.g., plot, background, genre, etc. Several similarity measure methods, notably vector space model based and category hierarchy model based similarity measure schemes, have been proposed for the purpose of data clustering and example-based retrieval. Each method has advantages and shortcomings of its own in TV content similarity measure. In this paper, we propose a hybrid approach for TV content similarity measure, which combines both vector space model and category hierarchy model. The hybrid measure proposed here makes the most of TV metadata information and takes advantage of the two similarity measurements. It measures TV content similarity from the semantic level other than the physical level. Furthermore, we propose an adaptive strategy for setting the combination parameters. The experimental results showed that using the hybrid similarity measure proposed here is superior to using either alone for TV content clustering and example-based retrieval.
KW - Category hierarchy model
KW - Clustering
KW - Example-based retrieval
KW - Similarity measure
KW - TV-anytime
KW - Vector space model
UR - http://www.scopus.com/inward/record.url?scp=77956617061&partnerID=8YFLogxK
U2 - 10.1007/s00530-010-0196-7
DO - 10.1007/s00530-010-0196-7
M3 - 文章
AN - SCOPUS:77956617061
SN - 0942-4962
VL - 16
SP - 231
EP - 241
JO - Multimedia Systems
JF - Multimedia Systems
IS - 4-5
ER -