TY - GEN
T1 - SNDocRank
T2 - 2010 ACM SIGMM International Conference on Multimedia Information Retrieval, MIR 2010
AU - Gou, Liang
AU - Chen, Hung Hsuan
AU - Kim, Jung Hyun
AU - Zhang, Xiaolong
AU - Giles, C. Lee
PY - 2010
Y1 - 2010
N2 - Multimedia ranking algorithms are usually user-neutral and measure the importance and relevance of documents by only using the visual contents and meta-data. However, users' interests and preferences are often diverse, and may demand different results even with the same queries. How can we integrate user interests in ranking algorithms to improve search results? Here, we introduce Social Network Document Rank (SNDocRank), a new ranking framework that considers a searcher's social network, and apply it to video search. SNDocRank integrates traditional tf-idf ranking with our Multi-level Actor Similarity (MAS) algorithm, which measures the similarity between social networks of a searcher and document owners. Results from our evaluation study with a social network and video data from YouTube show that SNDocRank offers search results more relevant to user's interests than other traditional ranking methods.
AB - Multimedia ranking algorithms are usually user-neutral and measure the importance and relevance of documents by only using the visual contents and meta-data. However, users' interests and preferences are often diverse, and may demand different results even with the same queries. How can we integrate user interests in ranking algorithms to improve search results? Here, we introduce Social Network Document Rank (SNDocRank), a new ranking framework that considers a searcher's social network, and apply it to video search. SNDocRank integrates traditional tf-idf ranking with our Multi-level Actor Similarity (MAS) algorithm, which measures the similarity between social networks of a searcher and document owners. Results from our evaluation study with a social network and video data from YouTube show that SNDocRank offers search results more relevant to user's interests than other traditional ranking methods.
UR - http://www.scopus.com/inward/record.url?scp=77952379148&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=77952379148&partnerID=8YFLogxK
U2 - 10.1145/1743384.1743443
DO - 10.1145/1743384.1743443
M3 - Conference contribution
AN - SCOPUS:77952379148
SN - 9781605588155
T3 - MIR 2010 - Proceedings of the 2010 ACM SIGMM International Conference on Multimedia Information Retrieval
SP - 367
EP - 376
BT - MIR 2010 - Proceedings of the 2010 ACM SIGMM International Conference on Multimedia Information Retrieval
Y2 - 29 March 2010 through 31 March 2010
ER -