mediaeval 2015 - ohsu @ mediaeval 2015: adapting textual techniques to multimedia search
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OHSU @ MediaEval 2015: Adapting Textual Techniques to
Multimedia Search
Shiran Dudy and Steven Bedrick !
Center for Spoken Language Understanding Oregon Health & Science University
Concept
Y. Zhu et al. Learning for search result diversification. In Proceedings of the 37th international ACM SIGIR conference on Research & development in information retrieval, pages 293–302. ACM, 2014.
Algorithm
Y. Zhu et al. Learning for search result diversification. In Proceedings of the 37th international ACM SIGIR conference on Research & development in information retrieval, pages 293–302. ACM, 2014.
r d
Algorithm
Y. Zhu et al. Learning for search result diversification. In Proceedings of the 37th international ACM SIGIR conference on Research & development in information retrieval, pages 293–302. ACM, 2014.
Relevance feature vector
r d
Algorithm
Y. Zhu et al. Learning for search result diversification. In Proceedings of the 37th international ACM SIGIR conference on Research & development in information retrieval, pages 293–302. ACM, 2014.
Relevance feature vector
r d
Diversity relationships with selected docs
Algorithm
Y. Zhu et al. Learning for search result diversification. In Proceedings of the 37th international ACM SIGIR conference on Research & development in information retrieval, pages 293–302. ACM, 2014.
Relevance feature vector
r d
Diversity relationships with selected docs
Weight vectors
Features
Relevance LSA (100) user credibility - “visualScore” - “faceProportion” - “tagSpecificity” - “uniqueTags” - “locationSimilarity”
Diversity LDA (20) cosine disimilarity “csd” (L2) “hog” (Bhatacharyya) “cn” (L2) “cm” (Canberra) “lbp” (χ2) “glr” (L1)
Learning Algorithm
Y. Zhu et al. Learning for search result diversification. In Proceedings of the 37th international ACM SIGIR conference on Research & development in information retrieval, pages 293–302. ACM, 2014.
To extract the weight vectors Wr and Wd we use
Learning Algorithm
Y. Zhu et al. Learning for search result diversification. In Proceedings of the 37th international ACM SIGIR conference on Research & development in information retrieval, pages 293–302. ACM, 2014.
That is with the loss function:
So we compute their gradients by simply taking their derivative
Learning Algorithm
Y. Zhu et al. Learning for search result diversification. In Proceedings of the 37th international ACM SIGIR conference on Research & development in information retrieval, pages 293–302. ACM, 2014.
Diversity Feature Vector Rij
subtopic diversity
text diversity
title diversity
anchor text diversity
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content and importance
Diversity Feature Vector Rij
subtopic diversity
text diversity
title diversity
anchor text diversity
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Diversity Feature Vector Rij
subtopic diversity
text diversity
title diversity
anchor text diversity
ODP-based diversity
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Diversity Feature Vector Rij
subtopic diversity
text diversity
title diversity
anchor text diversity
ODP-based diversity
!
Diversity Feature Vector Rij
subtopic diversity
text diversity
title diversity
anchor text diversity
ODP-based diversity
linked-based diversity
Diversity Feature Vector Rij
subtopic diversity
text diversity
title diversity
anchor text diversity
ODP-based diversity
linked-based diversity Generating Diverse and Representative Image Search Results for Landmarks, 2008, Lyndon Kennedy
Diversity Feature Vector Rij
subtopic diversity
text diversity
title diversity
anchor text diversity
ODP-based diversity
linked-based diversity
Diversity Feature Vector Rij
subtopic diversity
text diversity
title diversity
anchor text diversity
ODP-based diversity
linked-based diversity
url-based diversity
Diversity Feature Vector Rij
subtopic diversity
text diversity
title diversity
anchor text diversity
ODP-based diversity
linked-based diversity
url-based diversity