A Study of the Impact of Personality and Social Psychology on Mental Health

With the continuous development and progress of economy,people's living standard and culture level have been improved,but with it,there are also various kinds of life stress,study stress and work stress,which make people's mental health problems in life more andmore prominent,and how to improve students'mental health level is the main task of each education stage.The current psychology discipline system has been perfected,including personality psychology and social psychology,which are effectively applied in the actual mental health education or psychological guidance work,and are key disciplines to improve people's mental health.The theoretical system of personality and social psychology consists of three theories:the theory of planned behavior,the theory of explanatory levels,and the theory of self-determination,all of which have an irreplaceable influence on mental health.In this regard,this paper combines relevant literature and work experience to study in depth the influence of personality ansocial psychology on mental health.

Recommending Personalized POIs from Location Based Social Network

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.

A Trustworthy Group Identifying Trust Metric for P2P Service Sharing Economy Based on Personal Social Network of Users

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.