scholarly journals A Novel Mobile Video Community Discovery Scheme Using Ontology-Based Semantical Interest Capture

2016 ◽  
Vol 2016 ◽  
pp. 1-12 ◽  
Author(s):  
Ruiling Zhang ◽  
Shengwu Xiong

Leveraging network virtualization technologies, the community-based video systems rely on the measurement of common interests to define and steady relationship between community members, which promotes video sharing performance and improves scalability community structure. In this paper, we propose a novel mobile Video Community discovery scheme using ontology-based semantical interest capture (VCOSI). An ontology-based semantical extension approach is proposed, which describes video content and measures video similarity according to video key word selection methods. In order to reduce the calculation load of video similarity, VCOSI designs a prefix-filtering-based estimation algorithm to decrease energy consumption of mobile nodes. VCOSI further proposes a member relationship estimate method to construct scalable and resilient node communities, which promotes video sharing capacity of video systems with the flexible and economic community maintenance. Extensive tests show how VCOSI obtains better performance results in comparison with other state-of-the-art solutions.

2020 ◽  
Vol 44 (6) ◽  
Author(s):  
Rituparna Das ◽  
Nidhi Manaktala ◽  
Tanupriya Bhatia ◽  
Shubham Agarwal ◽  
Srikant Natarajan ◽  
...  

2012 ◽  
Vol 8 (2) ◽  
pp. 249-270 ◽  
Author(s):  
Sae Fujii ◽  
Akira Uchiyama ◽  
Takaaki Umedu ◽  
Hirozumi Yamaguchi ◽  
Teruo Higashino

2013 ◽  
Vol 321-324 ◽  
pp. 2902-2905 ◽  
Author(s):  
Chang Bin Li ◽  
Hua Li ◽  
Peng Wei Wang

Tag collision happens when multiple tags are energized simultaneously, reflect their respective signals back to the reader at the same time, and the reader is unable to differentiate these signals. This problem causes decrease of the RFID efficiency especially in the tag-intensive RFID system. The dynamic frame slotted aloha (DFSA) algorithm is the most appropriate solution for this problem, which is based on an accurate tags number estimate method. A novel forward predicting and backward verifying adaptive precise tags number estimation algorithm is proposed in this paper. Experiment results show that it is more effective than others.


Author(s):  
Huan Yan ◽  
Xiangning Chen ◽  
Chen Gao ◽  
Yong Li ◽  
Depeng Jin

Existing web video systems recommend videos according to users' viewing history from its own website. However, since many users watch videos in multiple websites, this approach fails to capture these users' interests across sites. In this paper, we investigate the user viewing behavior in multiple sites based on a large scale real dataset. We find that user interests are comprised of cross-site consistent part and site-specific part with different degrees of the importance. Existing linear matrix factorization recommendation model has limitation in modeling such complicated interactions. Thus, we propose a model of Deep Attentive Probabilistic Factorization (DeepAPF) to exploit deep learning method to approximate such complex user-video interaction. DeepAPF captures both cross-site common interests and site-specific interests with non-uniform importance weights learned by the attentional network. Extensive experiments show that our proposed model outperforms by 17.62%, 7.9% and 8.1% with the comparison of three state-of-the-art baselines. Our study provides insight to integrate user viewing records from multiple sites via the trusted third party, which gains mutual benefits in video recommendation.


Author(s):  
Jari Multisilta ◽  
Arttu Perttula ◽  
Marko Suominen ◽  
Antti Koivisto
Keyword(s):  

Sign in / Sign up

Export Citation Format

Share Document