The User-Group Based Recommendation for the Diverse Multimedia Contents in the Social Network Environments

Abstract

It has become general that consumers freely create contents and spend them for themselves on Web services in Web 2.0 environments. This research introduces the personalized contents recommendation system that answers the recent propensity trends about the multimedia contents based on web environments. The proposed system mined the usage history patterns of the target user and the related users from the web and proposed users' favorable contents which are unknown to users. The Social relationships in social network environments help the system to gather the meaningful history for users. And the system also recommended the various multimedia contentsarticles, TV programs, advertisements, video and audio, the system could apply the various users' history about the various contents. Because the system is applied to the social network environments, the information about friends and groups can be used in the recommendation algorithm.

Pricing and resource allocation for multimedia social network in cloud environments

“…User cooperation/collaboration is also applied in multimedia social network. In [15], users were divided into different groups according to their own preferences and a content ranker. This paper also proposed a cluster filtering mechanism to collect useful data for multimedia contents recommendation.…”

Group Recommendation Systems Based on External Social‐Trust Networks

“…One of the important influencing factors of the GRS is the social relationship among group [8,9,31,32]. Research shows that users prefer to accept the recommendation of trusted users rather than anonymous user [5].…”

Representação, extração e avaliação de interações entre usuários de redes sociais online

“…A recomendação de ligações sociais e conteúdos multimídia em Redes Sociais Online é um tema explorado em pesquisas recentes [Esmaeili et al, 2011], [Anastopoulos et al, 2011], [Shin et al, 2011], [Yang et al, 2012], [Chaoji et al, 2012]. O método e a técnica apresentados nesta tese podem ser alinhados com essas iniciativas, por exemplo, sugerindo ligações entre usuários que tem um padrão de interação social similar ou que interage com frequência com um amigo em comum, ou ainda, recomendar conteúdos como imagens, vídeos, músicas e localidades entre usuários que têm um mesmo padrão de interação ou têm um grupo de amigos em comum.…”