Analysis of the Information Value of User Connections for Video Recommendations in a Social Network

FutureTV-2011: Making Television Personal & Social 2011.

工业技术;计算机科学

Toon De Pessemier ; Simon Dooms ; Joost Roelandts ; Luc Martens

Others  :  http://ceur-ws.org/Vol-720/DePessemier.pdf
PID  :  42412

来源: CEUR

【 摘 要 】

The abundance of information and the related difficulty to discover interesting (video) content has complicated the selection process for end-users. Recommender systems try to assist in thiscontent-selection process by using intelligent personalisation techniques which filter the information. However, most commonly-used recommendation algorithms, like collaborative filtering, are not optimized for social networks which contain valuable information about the user's friend connections and the structure of personal relationship networks. Therefore, this paper analyses the data set of a commercially-deployed social network and investigates the information value of user-to-user relations and video interaction behaviour in the user's friend network. The results prove that video selection in a social network is significantly in uenced by the consumption behaviour in the personal network of the user. This information might be incorporated as an additional knowledge source into recommender systems, thereby improving the accuracy of the video suggestions. Moreover, the size of the user's social network has a significant positive correlation with the popularity of the user's uploaded videos. As a result, users having a large social network, i.e. be connected to a huge number of people, act as \hubs" of information. Video content uploaded or distributed by these users has a high visibility and acceptance rate on social networks.