Few studies have examined the relationship between personality traits and social networking sites (SNSs) with a dominant concentration on the personality alterations under SNSs influence. The relationship between personality and privacy control was less focused and discussed. In order to figure out the internal mechanism of such link among youth SNSs users, the Theory of Planned Behavior (TPB) was extended by including Five-Factor Model of Personality to explore how personality traits interact with privacy control behavior on SNSs. The investigation using (...) the theoretical method mentioned led to several hypotheses which were later assessed by an online study conducted within randomly chosen college students (N = 201) from two randomly chosen universities in China. This sampling strategy was designed to mimic the situation of targeted research population in the most reasonable way. The results suggested neuroticism and openness predicted SNSs privacy. Neuroticism and openness predicted “networked privacy” was also found. Theoretical implications of these findings were addressed. (shrink)

Although personal and group recommendation systems have been quickly developed recently, challenges and limitations still exist. In particular, users constantly explore new items and change their preferences throughout time, which causes difficulties in building accurate user profiles and providing precise recommendation outcomes. In this context, this study addresses the time awareness of the user preferences and proposes a hybrid recommendation approach for both individual and group recommendations to better meet the user preference changes and thus improve the recommendation performance. The (...) experimental results show that the proposed approach outperforms several baseline algorithms in terms of precision, recall, novelty, and diversity, in both personal and group recommendations. Moreover, it is clear that the recommendation performance can be largely improved by capturing the user preference changes in the study. These findings are beneficial for increasing the understanding of the user dynamic preference changes in building more precise user profiles and expanding the knowledge of developing more effective and efficient recommendation systems. (shrink)