Optimal Ranking for Video Recommendation

Author(s):  
Zeno Gantner ◽  
Christoph Freudenthaler ◽  
Steffen Rendle ◽  
Lars Schmidt-Thieme
1992 ◽  
Vol 02 (01) ◽  
pp. 31-41 ◽  
Author(s):  
PILAR DE LA TORRE ◽  
RAYMOND GREENLAW ◽  
TERESA M. PRZYTYCKA

This paper places the optimal tree ranking problem in [Formula: see text]. A ranking is a labeling of the nodes with natural numbers such that if nodes u and v have the same label then there exists another node with a greater label on the path between them. An optimal ranking is a ranking in which the largest label assigned to any node is as small as possible among all rankings. An O(n) sequential algorithm is known. Researchers have speculated that this problem is P-complete. We show that for an n-node tree, one can compute an optimal ranking in O( log n) time using n2/ log n CREW PRAM processors. In fact, our ranking is super critical in that the label assigned to each node is absolutely as small as possible. We achieve these results by showing that a more general problem, which we call the super critical numbering problem, is in [Formula: see text]. No [Formula: see text] algorithm for the super critical tree ranking problem, approximate or otherwise, was previously known; the only known [Formula: see text] algorithm for optimal tree ranking was an approximate one.


Author(s):  
Xiaoran Xu ◽  
Laming Chen ◽  
Songpeng Zu ◽  
Hanning Zhou
Keyword(s):  

Author(s):  
Chanjal C

Predicting the relevance between two given videos with respect to their visual content is a key component for content-based video recommendation and retrieval. The application is in video recommendation, video annotation, Category or near-duplicate video retrieval, video copy detection and so on. In order to estimate video relevance previous works utilize textual content of videos and lead to poor performance. The proposed method is feature re-learning for video relevance prediction. This work focus on the visual contents to predict the relevance between two videos. A given feature is projected into a new space by an affine transformation. Different from previous works this use a standard triplet ranking loss that optimize the projection process by a novel negative-enhanced triplet ranking loss. In order to generate more training data, propose a data augmentation strategy which works directly on video features. The multi-level augmentation strategy works for video features, which benefits the feature relearning. The proposed augmentation strategy can be flexibly used for frame-level or video-level features. The loss function that consider the absolute similarity of positive pairs and supervise the feature re-learning process and a new formula for video relevance computation.


2021 ◽  
pp. 279-294
Author(s):  
Wei Zhuo ◽  
Kunchi Liu ◽  
Taofeng Xue ◽  
Beihong Jin ◽  
Beibei Li ◽  
...  

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