Paper Info
Title
SNDocRank: a social network-based video search ranking framework
Abstract
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.
Year
DOI
Venue
2010
10.1145/1743384.1743443
Multimedia Information Retrieval
Keywords
Field
DocType
social network,social network-based video search,video data,user interest,traditional ranking method,new ranking framework,traditional tf-idf ranking,ranking algorithm,video search,multimedia ranking algorithm,search result
Learning to rank,Social network,Information retrieval,Ranking,Computer science,Multimedia information retrieval,Ranking (information retrieval),Search ranking
Conference
Citations 
PageRank 
References 
13
0.81
20
Authors
5
Name
Order
Citations
PageRank
Liang Gou126915.49
Hung-Hsuan Chen224616.71
Jung-Hyun Kim3443.27
Xiaolong Zhang427821.91
C. Lee Giles5111541549.48