Social interaction based video recommendation: Recommending YouTube videos to facebook users

Nie, Bin; Zhang, Honggang; Liu, Yong

doi:10.1109/infcomw.2014.6849175

Cited by 19 publications

(10 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…By studying the previous recommendation algorithms about the similarity calculation of the friends and the target user, most of them [15,16,21,22] just simply compute a direct similarity based on Pearson correlation coefficient, and there is no distinction between direct similarity and indirect similarity. Wang et al [19] find out that lots of mentioned video recommendation algorithms ignore the attributes of recommendation so that the accuracy of the recommendation results are not very satisfactory.…”

Section: The Proposed Computing Methods Of Similarity Between Usersmentioning

confidence: 99%

“…By studying the previous recommendation algorithms about the similarity calculation of the friends and the target user, most of them just simply compute a direct similarity based on Pearson correlation coefficient, and there is no distinction between direct similarity and indirect similarity. Wang et al .…”

Section: The Trust Friends Computing Modelmentioning

confidence: 99%

“…And they predict the missing values in the user-item matrix based on user's friendship and rating records. To improve the recommendation precision for niche videos, Nie et al [16] propose a simple social interaction based video recommendation algorithm that takes advantage of user social interaction information.…”

Section: Related Workmentioning

confidence: 99%

See 2 more Smart Citations

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

Cui

Dong

et al. 2016

Concurrency and Computation

View full text Add to dashboard Cite

Summary Recently, social network has been one of the biggest information exchange platforms of the Internet. Moreover, the users in social network used to watch videos through social network application. To provide a proper recommended video list, the video recommendation algorithm for social network is becoming a hot research issue. On one hand, more and more researchers introduce the concept of trust into video recommendation algorithms. However, most of them only select the trust friends based on the similarity and neglect the characteristics of social network. On the other hand, most previous video recommendation algorithms are only based on the number that a video is viewed to evaluate a video's quality. They do not make good use of the social relationship in social network and the video's reputation. This paper mainly focuses on the challenge that the effectiveness and performance of current video recommendation algorithm in social network cannot satisfy the users. In this paper, we propose a novel video recommendation algorithm based on the combination of video content and social network. Our proposed algorithm consists of the trust friends computing model and video's quality evaluation model. The trust friends computing method takes into account similarity between users, interaction between users, and the active degree of a user. In our video's quality evaluation model, we combine the acceptance ratio of a video with a video's reputation. The video can be given an appropriate rating score through this model. We design corresponding trust friends computing algorithm and video recommendation algorithm respectively for two proposed models. Our integral video recommendation algorithm consists of these two algorithms. The experimental results indicate that the performance and effectiveness of our algorithm are better than those of two classical video recommendation algorithms (i.e., user‐based collaborative filtering algorithm and TBR‐d algorithm), in terms of precision, recall and F1‐measure. Copyright © 2016 John Wiley & Sons, Ltd.

show abstract

Section: The Proposed Computing Methods Of Similarity Between Usersmentioning

confidence: 99%

Section: The Trust Friends Computing Modelmentioning

confidence: 99%

See 1 more Smart Citation

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

Cui

Dong

et al. 2016

Concurrency and Computation

View full text Add to dashboard Cite

show abstract

“…In this study [295] a case study of recommending YouTube videos to Facebook users based on their social interactions is conducted. They first measure social interactions related to YouTube videos among Facebook users.…”

Section: Related Workmentioning

confidence: 99%

Semantically-enhanced advertisement recommender systems in social networks

Pazahr

Zapater

García‐Sánchez

et al. 2016

Proceedings of the 18th International Conference on Information Integration and Web-Based Applications and Services

View full text Add to dashboard Cite

“…Social network analysis (SNA) is a research field which deals with analysing such networks and extracting useful information about people described within, with the analysis being mostly focused on user interactions. There are numerous possible applications: by analysing social networks sociologists and social psychologists are trying to explain how people's thoughts, feelings and behaviours are influenced by presence of others [1,2]; recommender systems can use it to make customized and novel recommendations [3,4];…”

Section: Introduction and Related Workmentioning

confidence: 99%

Exploratory analysis of pairwise interactions in online social networks

2017

View full text Add to dashboard Cite

In the last few decades sociologists were trying to explain human behaviour by analysing social networks, which requires access to data about interpersonal relationships. This represented a big obstacle in this research field until the emergence of online social networks (OSNs), which vastly facilitated the process of collecting such data. Nowadays, by crawling public profiles on OSNs, it is possible to build a social graph where "friends" on OSN become represented as connected nodes. OSN connection does not necessarily indicate a close real-life relationship, but using OSN interaction records may reveal real-life relationship intensities, a topic which inspired a number of recent researches. Still, published research currently lacks an extensive exploratory analysis of OSN interaction records, i.e. a comprehensive overview of users' interaction via different ways of OSN interaction. In this paper, we provide such an overview by leveraging results of conducted extensive social experiment which managed to collect records for over 3200 Facebook users interacting with over 1,400,000 of their friends. Our exploratory analysis focuses on extracting population distributions and correlation parameters for 13 interaction parameters, providing valuable insight into OSN interaction for future researches aimed at this field of study. ARTICLE HISTORY

show abstract

Social interaction based video recommendation: Recommending YouTube videos to facebook users

Cited by 19 publications

References 9 publications

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

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

Semantically-enhanced advertisement recommender systems in social networks

Exploratory analysis of pairwise interactions in online social networks

Contact Info

Product

Resources

About