## 1 Introduction  
The explosive growth in social network services has now made it a common place for communication among various communities and a platform to build relationships through interactions. It also supports mutual exchange of information based on common interests and could be a great source of data for information recommendation [1]. Although it may be greatly desired to provide such information recommendation services based on vast amounts of social network information, there are several difficulties in making such an information recommendation to appear to be intelligent for the user. There are vast amounts of social relationships in a social network and it is difficult to filter the essential information that meets the users' needs directly from the massive amounts of produced data. The demand from various users to find the information that they need has increased greatly. To obtain the desired information, many users search the information produced by others with social relationships that meet their specific requirements and needs [2; 3; 4]. Information recommendation has been mostly based on a collaborative filtering method that uses a matrix of users and items and contains user ratings on items. This matrix is used to identify similar users and similar items of interest to recommend items to target users. However, this method has a cold start problem, which occurs when there is not enough rating information in the matrix. With social network services becoming available, information recommendation approaches tried to use the social network information to solve the cold start problem by using neighbors of users in the social network to identify users with similar taste [5]. Although this may work in some situations, we find that rather than just using simple neighbor information it is more effective to find users who are influencing the social network and consider those influencer's opinions with more weight in the information recommendation [6; 7]. In addition to this, compared to the static matrix of users and items used by collaborative filtering approaches, the social networks can capture the dynamic timelines of users and thus contain historical information of users' activities. Such activity logs containing the users' interaction time and frequencies can enhance the credibility of the information recommendation by giving more weight to users who provide opinions more actively with newer information. In this paper, we propose an information recommendation technique that reflects two specific types of analysis based on the social relations among the users participating in a social network. The two types of analysis are as follows. The first is user influence, which represents the analysis of factors affecting other users' behaviors. By analyzing the social network factors that affect user behavior, we can understand why users make decisions in specific situations and thus provide a better recommendation for the issue suggested. The second is user activity, which shows the level of activeness of the user who is eligible for creating the relevant information. The user activity in a social network is important for information recommendation because users who show specific activity patterns may be possessing valid and recent information useful to the recommendation. We have applied these proposed concepts on real Yelp [8] social network data to verify that the user influence and user activity concepts in social networks are useful for information recommendation. The research methodology for the proposed information recommendation techniques based on social network data is as follows. First, we establish the concepts of user influence and user activity in the context of social network data and explain their relationship with the information recommendation. We investigate the core components that can define the concepts of user influence and user activity based on social network data. Formulas including these core components, to calculate the user influence and user activity, are devised.