A novel video recommendation system based on efficient retrieval of human actions

2016 ◽  
Vol 457 ◽  
pp. 607-623 ◽  
Author(s):  
Mohsen Ramezani ◽  
Farzin Yaghmaee
Keyword(s):  
Recommendation System ◽  
Human Actions ◽  
Efficient Retrieval ◽  
Video Recommendation

scholarly journals Personalized Mobile Video Recommendation Based on User Preference Modeling by Deep Features and Social Tags

2019 ◽  
Vol 9 (18) ◽  
pp. 3858
Author(s):  
Jiafeng Li ◽  
Chenhao Li ◽  
Jihong Liu ◽  
Jing Zhang ◽  
Li Zhuo ◽  
...  
Keyword(s):  
Recommendation System ◽  
Moving Average ◽  
Likelihood Estimation ◽  
User Preference ◽  
Personalized Recommendation ◽  
Mobile Video ◽  
Preference Modeling ◽  
Social Tags ◽  
Personalized Video ◽  
Video Recommendation

With the explosive growth of mobile videos, helping users quickly and effectively find mobile videos of interest and further provide personalized recommendation services are the developing trends of mobile video applications. Mobile videos are characterized by their wide variety, single content, and short duration, and thus traditional personalized video recommendation methods cannot produce effective recommendation performance. Therefore, a personalized mobile video recommendation method is proposed based on user preference modeling by deep features and social tags. The main contribution of our work is three-fold: (1) deep features of mobile videos are extracted by an improved exponential linear units-3D convolutional neural network (ELU-3DCNN) for representing video content; (2) user preference is modeled by combining user preference for deep features with user preference for social tags that are respectively modeled by maximum likelihood estimation and exponential moving average method; (3) a personalized mobile video recommendation system based on user preference modeling is built after detecting key frames with a differential evolution optimization algorithm. Experiments on YouTube-8M dataset have shown that our method outperforms state-of-the-art methods in terms of both precision and recall of personalized mobile video recommendation.

Affivir: An affect-based Internet video recommendation system

2013 ◽  
Vol 120 ◽  
pp. 422-433 ◽  
Author(s):  
Jianwei Niu ◽  
Xiaoke Zhao ◽  
Like Zhu ◽  
Haiying Li
Keyword(s):  
Recommendation System ◽  
Internet Video ◽  
Video Recommendation

scholarly journals Design of Context Cluster and Load Balanced Video Recommendation System in Cloud Computing

2022 ◽  
Vol 12 (1) ◽  
pp. 0-0
Keyword(s):  
Online Social Networks ◽  
Cloud Storage ◽  
Recommendation System ◽  
Personalized Recommendation ◽  
Multimedia Services ◽  
Rule Based ◽  
Selection Technique ◽  
Server Selection ◽  
Network Overhead ◽  
Video Recommendation

Nowadays, in online social networks, there is an instantaneous extension of multimedia services and there are huge offers of video contents which has hindered users to acquire their interests. To solve these problem different personalized recommendation systems had been suggested. Although, all the personalized recommendation system which have been suggested are not efficient and they have significantly retarded the video recommendation process. So to solve this difficulty, context extractor based video recommendation system on cloud has been proposed in this paper. Further to this the system has server selection technique to handle the overload program and make it balanced. This paper explains the mechanism used to minimize network overhead and recommendation process is done by considering the context details of the users, it also uses rule based process and different algorithms used to achieve the objective. The videos will be stored in the cloud and through application videos will be dumped into cloud storage by reading, coping and storing process.

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