SNDocRank: a social network-based video search ranking framework

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.

[1]  Jiebo Luo,et al.  Mining GPS traces and visual words for event classification , 2008, MIR '08.

[2]  Mika Rautiainen,et al.  Comparison of Visual Features and Fusion Techniques in Automatic Detection of Concepts from News Video , 2005, 2005 IEEE International Conference on Multimedia and Expo.

[3]  M. Newman,et al.  Finding community structure in very large networks. , 2004, Physical review. E, Statistical, nonlinear, and soft matter physics.

[4]  Gerard Salton,et al.  Automatic Text Processing: The Transformation, Analysis, and Retrieval of Information by Computer , 1989 .

[5]  Mor Naaman,et al.  How flickr helps us make sense of the world: context and content in community-contributed media collections , 2007, ACM Multimedia.

[6]  Alessio Malizia,et al.  Visual tag authoring: picture extraction via localized, collaborative tagging , 2008, AVI '08.

[7]  B. S. Manjunath,et al.  Spirittagger: a geo-aware tag suggestion tool mined from flickr , 2008, MIR '08.

[8]  Gerard Salton,et al.  Term-Weighting Approaches in Automatic Text Retrieval , 1988, Inf. Process. Manag..

[9]  Stanley Wasserman,et al.  Social Network Analysis: Methods and Applications , 1994, Structural analysis in the social sciences.

[10]  Rong Yan,et al.  Multimedia Search with Pseudo-relevance Feedback , 2003, CIVR.

[11]  Xiaolong Zhang,et al.  MobiSNA: a mobile video social network application , 2009, MobiDE.

[12]  Allan Hanbury,et al.  Overview of the ImageCLEFphoto 2007 Photographic Retrieval Task , 2008, CLEF.

[13]  H. White,et al.  “Structural Equivalence of Individuals in Social Networks” , 2022, The SAGE Encyclopedia of Research Design.

[14]  Yi-Hsuan Yang,et al.  ContextSeer: context search and recommendation at query time for shared consumer photos , 2008, ACM Multimedia.

[15]  M. McPherson,et al.  Birds of a Feather: Homophily in Social Networks , 2001 .

[16]  Timo Ojala,et al.  Analysing the performance of visual, concept and text features in content-based video retrieval , 2004, MIR '04.

[17]  M E J Newman,et al.  Finding and evaluating community structure in networks. , 2003, Physical review. E, Statistical, nonlinear, and soft matter physics.

[18]  Shih-Fu Chang,et al.  Video search reranking through random walk over document-level context graph , 2007, ACM Multimedia.

[19]  Don Coppersmith,et al.  Matrix multiplication via arithmetic progressions , 1987, STOC.

[20]  Shih-Fu Chang,et al.  A reranking approach for context-based concept fusion in video indexing and retrieval , 2007, CIVR '07.

[21]  Jaana Kekäläinen,et al.  IR evaluation methods for retrieving highly relevant documents , 2000, SIGIR '00.

[22]  J. R. Landis,et al.  The measurement of observer agreement for categorical data. , 1977, Biometrics.

[23]  Dong Xu,et al.  Columbia University TRECVID-2006 Video Search and High-Level Feature Extraction , 2006, TRECVID.

[24]  M. Newman,et al.  Vertex similarity in networks. , 2005, Physical review. E, Statistical, nonlinear, and soft matter physics.

[25]  Abby Goodrum,et al.  Image Information Retrieval: An Overview of Current Research , 2000, Informing Sci. Int. J. an Emerg. Transdiscipl..

[26]  Hugo Jair Escalante,et al.  Late fusion of heterogeneous methods for multimedia image retrieval , 2008, MIR '08.

[27]  Marcel Worring,et al.  Content-Based Image Retrieval at the End of the Early Years , 2000, IEEE Trans. Pattern Anal. Mach. Intell..