A video recommendation algorithm based on the combination of video content and social network

2016 ◽  
Vol 29 (14) ◽  
pp. e3900 ◽  
Author(s):  
Laizhong Cui ◽  
Linyong Dong ◽  
Xianghua Fu ◽  
Zhenkun Wen ◽  
Nan Lu ◽  
...  
Keyword(s):  
Social Network ◽  
Video Content ◽  
Recommendation Algorithm ◽  
Video Recommendation

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.

scholarly journals THE ALGORITHM PLAYGROUND: A CONTENT ANALYSIS OF USER-PRODUCED CHILDREN’S VIDEOS ON YOUTUBE

2021 ◽  
Author(s):  
Kalia Vogelman-Natan
Keyword(s):  
Content Analysis ◽  
Pretend Play ◽  
Online Video ◽  
Video Content ◽  
Recommendation Algorithm ◽  
Mobile Media ◽  
Future Studies ◽  
Research Questions ◽  
Device Use ◽  
Video Recommendation

With early-childhood mobile media device use on the rise, online video content plays an ever-increasing role in children’s lives. Of the wide variety of content available to children, user-produced videos on YouTube seem to be most popular. However, due to the platform’s size and the overwhelming number of child-targeted videos found on YouTube, scholars have been struggling with how to approach and study this topic. This study aims to address the gap in research by analyzing prevalent user-produced children’s videos on YouTube, with research questions focusing on video genres, their features, and content themes. Drawing on YouTube’s popularity-measurements and video recommendation algorithm, a corpus of 100 user-produced videos targeted to children was assembled. A content analysis of these videos led to the identification and conceptualization of 13 distinct genres of user-produced children’s videos: unboxing, surprise eggs, finger family, play-doh, nursery rhymes, kids songs, learning, pretend play (enactment), pretend play (toys), storytelling, arts & crafts, entertainer in character, and process repetition. Furthermore, the findings indicate that there are often unique interplays between genre type and the content, the production format, and the overall quality and educational rating. In addition to shedding light on the importance of studying child-targeted content on YouTube, this study’s main contribution is a typological map of the user-produced children’s video ecosystem that future studies from various fields can draw on.

Improving Collaborative Filtering Algorithms

2016 ◽  
Vol 7 (3) ◽  
pp. 99-118 ◽  
Author(s):  
Firas Ben Kharrat ◽  
Aymen Elkhleifi ◽  
Rim Faiz
Keyword(s):  
Social Network ◽  
Web Sites ◽  
Opinion Mining ◽  
Semantic Analysis ◽  
Real Data ◽  
Data Sets ◽  
Recommendation Algorithm ◽  
Suggested Approach ◽  
Opinion Score ◽  
Positive Results

This paper puts forward a new recommendation algorithm based on semantic analysis as well as new measurements. Like Facebook, Social network is considered as one of the most well-prominent Web 2.0 applications and relevant services elaborating into functional ways for sharing opinions. Thereupon, social network web sites have since become valuable data sources for opinion mining. This paper proposes to introduce an external resource a sentiment from comments posted by users in order to anticipate recommendation and also to lessen the cold-start problem. The originality of the suggested approach means that posts are not merely characterized by an opinion score, but receive an opinion grade notion in the post instead. In general, the authors' approach has been implemented with Java and Lenskit framework. The study resulted in two real data sets, namely MovieLens and TripAdvisor, in which the authors have shown positive results. They compared their algorithm to SVD and Slope One algorithms. They have fulfilled an amelioration of 10% in precision and recall along with an improvement of 12% in RMSE and nDCG.

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 Dynamical Modeling, Analysis, and Control of Information Diffusion over Social Networks: A Deep Learning-Based Recommendation Algorithm in Social Network

2020 ◽  
Vol 2020 ◽  
pp. 1-8
Author(s):  
Kefei Cheng ◽  
Xiaoyong Guo ◽  
Xiaotong Cui ◽  
Fengchi Shan
Keyword(s):  
Social Networks ◽  
Deep Learning ◽  
Social Network ◽  
Information Diffusion ◽  
Recommendation Algorithm ◽  
Central Sequence ◽  
Modeling Analysis ◽  
News Recommendation ◽  
Browsing Behavior ◽  
And Control

The recommendation algorithm can break the restriction of the topological structure of social networks, enhance the communication power of information (positive or negative) on social networks, and guide the information transmission way of the news in social networks to a certain extent. In order to solve the problem of data sparsity in news recommendation for social networks, this paper proposes a deep learning-based recommendation algorithm in social network (DLRASN). First, the algorithm is used to process behavioral data in a serializable way when users in the same social network browse information. Then, global variables are introduced to optimize the encoding way of the central sequence of Skip-gram, in which way online users’ browsing behavior habits can be learned. Finally, the information that the target users’ have interests in can be calculated by the similarity formula and the information is recommended in social networks. Experimental results show that the proposed algorithm can improve the recommendation accuracy.

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