Social factors in group recommender systems

Abstract

In this article we review the existing techniques in group recommender systems and we propose some improvement based on the study of the different individual behaviors when carrying out a decision-making process. Our method includes an analysis of group personality composition and trust between each group member to improve the accuracy of group recommenders. This way we simulate the argumentation process followed by groups of people when agreeing on a common activity in a more realistic way. Moreover, we reflect how they expect the system to behave in a long term recommendation process. This is achieved by including a memory of past recommendations that increases the satisfaction of users whose preferences have not been taken into account in previous recommendations.

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].…”

“…Though some papers [4][5][6][7]9] considered the social factors such as personality, trust, personal expertise, and social status of the members; once the information was acquired, the steps of the social networking group recommendation systems were as follows: At first, fetching the list of preference of all group members by individual recommendation system; then, increasing or decreasing the weight of a group of members of the article via social factors; finally, getting the ultimate results of group recommendation systems by some aggregation strategies, which consider the individual recommendation and weight. These methods utilize the social factors more or less; group members' weight is considered in ones; it means that a few of members would be ignored because of some group members play a decisive role, leading to the tendency to influence influential members.…”

“…As to previous group recommendations in social networks, they consider the strength of the relationship between group members [4,5] and the impact of social network information on each group member [7][8][9], hence generating group recommendation by the aggregation strategy. At present, the main impact of the social network on the group recommendation systems is the social influence of the group members.…”

“…The current social network group recommendation systems consider both the strength of the relationship between the members of the group into account [4][5][6] and the influence of social network information on each group members [7][8][9] and finally generate group recommenders through aggregation strategies. At present, the main influence of social network is the social impact of the group members on the group recommendation systems.…”

“…In addition, whole group members are asked to be in the same social network on present social network 2 Wireless Communications and Mobile Computing recommendation system [4,5,[7][8][9], and the members of other social networks could be ignored. The method proposed in this paper is that some users who dislike communicating with others on social network and are not in the same social network can benefit from this as well.…”