scholarly journals A Personalized Video Recommendation Algorithm Based on Complex Network

2018 ◽  
Author(s):  
Shuxia Pang ◽  
Weifang Wang ◽  
Honglei Zhu
Keyword(s):  
Complex Network ◽  
Recommendation Algorithm ◽  
Personalized Video ◽  
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.

A Video Recommendation Algorithm Based on Hyperlink-Graph Model

2017 ◽  
Vol 5 (3) ◽  
pp. 49-63
Author(s):  
Songtao Shang ◽  
Wenqian Shang ◽  
Minyong Shi ◽  
Shuchao Feng ◽  
Zhiguo Hong
Keyword(s):  
Graph Model ◽  
User Interest ◽  
Recommendation Algorithm ◽  
Internet Users ◽  
Recommendation Algorithms ◽  
The Matrix ◽  
Personal Recommendation ◽  
Low Efficiency ◽  
Sports Videos ◽  
Video Recommendation

The traditional graph-based personal recommendation algorithms mainly depend the user-item model to construct a bipartite graph. However, the traditional algorithms have low efficiency, because the matrix of the algorithms is sparse and it cost lots of time to compute the similarity between users or items. Therefore, this paper proposes an improved video recommendation algorithm based on hyperlink-graph model. This method cannot only improve the accuracy of the recommendation algorithms, but also reduce the running time. Furthermore, the Internet users may have different interests, for example, a user interest in watching news videos, and at the same time he or she also enjoy watching economic and sports videos. This paper proposes a complement algorithm based on hyperlink-graph for video recommendations. This algorithm improves the accuracy of video recommendations by cross clustering in user layers.

scholarly journals A Novel Intelligent Recommendation Algorithm Based on Mass Diffusion

2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Guanglai Tian ◽  
Shuang Zhou ◽  
Gengxin Sun ◽  
Chih-Cheng Chen
Keyword(s):  
Bipartite Graph ◽  
Complex Network ◽  
Mass Diffusion ◽  
Online Information ◽  
Social Recommendation ◽  
Recommendation Algorithm ◽  
Multiple User

Social recommendation algorithm is a common tool for recommending interesting or potentially useful items to users amidst the sea of online information. The users usually have various relationships, each of which has its unique impact on the recommendation results. It is unlikely to make accurate recommendations solely based on one relationship. Based on user-item bipartite graph, this paper establishes a multisubnet composited complex network (MSCCN) of multiple user relationships and then extends the mass diffusion (MD) algorithm into a novel intelligent recommendation algorithm. Two public online datasets, namely, Epinions and FilmTrust, were selected to verify the effect of the proposed algorithm. The results show that the proposed intelligent recommendation algorithm with two types of relationships made much more accurate recommendations than that with a single relationship and the traditional MD algorithm.

An Empirical Study on Filter Bubbles in the YouTube Comments Network

2021 ◽  
Vol 9 (3) ◽  
pp. 52-65
Author(s):  
Dukjin Kim ◽  
Wooyoung Lee ◽  
Dohyung Kim ◽  
Gwangyong Gim
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Empirical Study ◽  
Information Acquisition ◽  
Recommendation Algorithm ◽  
Video Clips ◽  
Filter Bubble ◽  
Filter Bubbles ◽  
Video Recommendation

Some point out that the influence of YouTube's video recommendation algorithm is causing users to be exposed to only video clips in limited subjects or fields, especially to biased content with opinions that are tilted to one side. However, there is a lack of empirical research on filter bubbles as algorithms in YouTube have not been disclosed. This study indirectly demonstrated the phenomenon of filter bubble on YouTube by extracting comment-based content network between uploaders who posted videos and writers who wrote comments on the video by each subject of the contents. Also, this study analyzed communication patterns between users through social network analysis (SNA). According to the analysis, users' narrow information acquisition and communication phenomenon caused by the filter bubble in YouTube was found.

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