Various Methodologies for Micro-Video Recommendation System: A Survey

2018 ◽  
Author(s):  
Jyoti Raj ◽  
Amirul Hoque ◽  
Ashim Saha
Keyword(s):  
Recommendation System ◽  
System A ◽  
Video Recommendation

scholarly journals Product Recommendation System a Comprehensive Review

2021 ◽  
Vol 1022 ◽  
pp. 012021
Author(s):  
Jatin Sharma ◽  
Kartikay Sharma ◽  
Kaustubh Garg ◽  
Avinash Kumar Sharma
Keyword(s):  
Recommendation System ◽  
Comprehensive Review ◽  
Product Recommendation ◽  
System A

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.

Personalized query recommendation system : A genetic algorithm approach

2020 ◽  
Vol 23 (2) ◽  
pp. 523-535 ◽  
Author(s):  
Debaditya Barman ◽  
Ritam Sarkar ◽  
Anil Tudu ◽  
Nirmalya Chowdhury
Keyword(s):  
Genetic Algorithm ◽  
Recommendation System ◽  
System A ◽  
Query Recommendation ◽  
Genetic Algorithm Approach

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
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