With the quick growth of sharing economy, service sharing becomes a popular phenomenon in daily lives. However, some service providers give exaggerated information about their services on the Peer-to-Peer（P2 P） serv...With the quick growth of sharing economy, service sharing becomes a popular phenomenon in daily lives. However, some service providers give exaggerated information about their services on the Peer-to-Peer（P2 P） service sharing platforms to get more profits. How to identify a reliable service provider becomes a difficult challenge for users. In this paper, we propose a trustworthy group trust metric for P2 P service sharing（TMPSS） economy based on personal social network（PSN） of users. Deriving from Advogato group trust metric, it considers factors such as social circle similarity, preference similarity, interaction degree, ranks the reliable nodes in a target user＇s PSN, outputs an ordered set of reliable nodes, and prevents unreliable nodes from access PSN of honest users. Experimental results show that TMPSS has advantages over existing representative methods because it finds more reliable nodes, and counts against malicious nodes＇ attacks more effectively, and it is suitable for mobile transaction circumstances.展开更多
Location based social networks（ LBSNs） provide location specific data generated from smart phone into online social networks thus people can share their points of interest（ POIs）. POI collections are complex and c...Location based social networks（ LBSNs） provide location specific data generated from smart phone into online social networks thus people can share their points of interest（ POIs）. POI collections are complex and can be influenced by various factors,such as user preferences,social relationships and geographical influence. Therefore,recommending new locations in LBSNs requires to take all these factors into consideration. However,one problem is how to determine optimal weights of influencing factors in an algorithm in which these factors are combined. The user similarity can be obtained from the user check-in data,or from the user friend information,or based on the different geographical influences on each user＇s check-in activities. In this paper,we propose an algorithm that calculates the user similarity based on check-in records and social relationships,using a proposed weighting function to adjust the weights of these two kinds of similarities based on the geographical distance between users. In addition,a non-parametric density estimation method is applied to predict the unique geographical influence on each user by getting the density probability plot of the distance between every pair of user＇s check-in locations. Experimental results,using foursquare datasets,have shown that comparisons between the proposed algorithm and the other five baseline recommendation algorithms in LBSNs demonstrate that our proposed algorithm is superior in accuracy and recall,furthermore solving the sparsity problem.展开更多
基金Supported by the National Social Science Foundation of China(17BGL201)
文摘With the quick growth of sharing economy, service sharing becomes a popular phenomenon in daily lives. However, some service providers give exaggerated information about their services on the Peer-to-Peer（P2 P） service sharing platforms to get more profits. How to identify a reliable service provider becomes a difficult challenge for users. In this paper, we propose a trustworthy group trust metric for P2 P service sharing（TMPSS） economy based on personal social network（PSN） of users. Deriving from Advogato group trust metric, it considers factors such as social circle similarity, preference similarity, interaction degree, ranks the reliable nodes in a target user＇s PSN, outputs an ordered set of reliable nodes, and prevents unreliable nodes from access PSN of honest users. Experimental results show that TMPSS has advantages over existing representative methods because it finds more reliable nodes, and counts against malicious nodes＇ attacks more effectively, and it is suitable for mobile transaction circumstances.
文摘Location based social networks（ LBSNs） provide location specific data generated from smart phone into online social networks thus people can share their points of interest（ POIs）. POI collections are complex and can be influenced by various factors,such as user preferences,social relationships and geographical influence. Therefore,recommending new locations in LBSNs requires to take all these factors into consideration. However,one problem is how to determine optimal weights of influencing factors in an algorithm in which these factors are combined. The user similarity can be obtained from the user check-in data,or from the user friend information,or based on the different geographical influences on each user＇s check-in activities. In this paper,we propose an algorithm that calculates the user similarity based on check-in records and social relationships,using a proposed weighting function to adjust the weights of these two kinds of similarities based on the geographical distance between users. In addition,a non-parametric density estimation method is applied to predict the unique geographical influence on each user by getting the density probability plot of the distance between every pair of user＇s check-in locations. Experimental results,using foursquare datasets,have shown that comparisons between the proposed algorithm and the other five baseline recommendation algorithms in LBSNs demonstrate that our proposed algorithm is superior in accuracy and recall,furthermore solving the sparsity problem.
