## 2 Related Work  
### Recommendation Based on Social Networks  
Recommendation techniques are being actively researched with additional social network data becoming increasingly available. Most approaches try to integrate collaborative filtering with additional social network information [9]. The friend relationship is mostly the considered factor in social networks for the recommendation techniques [1]. This is due to the significance of the influence of friends on the users. Most of the information recommendation approaches have focused solely on the friend relationship in social networks and rather put more emphasis on providing an efficient way of calculating the friends of users to nicely integrate the results into the information recommendation in a timely manner [10].  
However, the social network has evolved and possesses much more information than just friend relationships and new kinds of patterns of influence are occurring among users in terms of marketing and purchasing behaviors led by so called influencers in the social network [7]. The notion of influence can be defined as a flow that brings change into the attitude or behavior of others. The concept of influence in many existing approaches just consider the quantitative aspects, such as the number of followers in a social network service [11; 12]. However, the influence concept should also deal with the qualitative aspects, such as reviews, interest, and others that cause the change in attitude or behavior of users. Our work considers several such components that could cause the change in user attitude and behavior.  
When considering influence in social networks, it is also very useful and efficient to put emphasis on the considerable minorities who contribute to influence on multiple users and induce the active behaviors from them [13; 14; 15]. Such an influence can be captured by incorporating experts existing in the social network.  
In networked structured environments, such as the Web, traditionally important pages or nodes in a network were identified by using PageRank algorithms [16], which are specific examples of general random walk algorithms that the rank importance of nodes only based on the static link structure [17; 18; 19; 20]. These algorithms were also applied to identify friends in a static social network for the information recommendation. In our approach, we identify influential users based on the characteristics of the dynamic interactions among users rather than just using static link information. As multimedia data are increasingly available on social networks these days, research issues on using the dynamic interaction among users and the multimedia contents for information recommendation is gaining interest [21].  
Information recommendation can be enhanced by using historical data from the social network, especially for applications in mobile environments. The mobile environment enables historical location and time data to be efficiently managed and can be combined with the social network data when performing information recommendation [22; 23]. By analyzing such historical information, the credibility of the information provided by a user could be more accurately estimated. Reference [24] uses a time weight in information recommendation to gradually decrease the rating provided by users based on time. The user activities in the social network may be monitored while historic information regarding the activities are maintained and become very useful for information recommendation. Social networks accumulate information about the user's past evaluation activities, making it possible to analyze the user's tendency of evaluations. Such historical information can include things such as the time that the evaluations are provided for, the content of the interactions, the location of the user, and the time that was spent in the social network. This historical activity information is used in our work to carefully estimate the activeness of a user, which can represent the validness or credibility of the information provided by the user.