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The popularity of social media has increased users' social visibility. However, users' limited ability to control information spread could compromise privacy. People care about how others perceive them. We examined people's concerns for others' evaluations on their behaviors under different degrees of privacy conditions. Using a variant of the dictator game, we induced dictators to self-select into pro-self or pro-social types and asked recipients to give written evaluations of the dictators. We varied the degree of personal information privacy by making the written content known to the corresponding dictators only, all dictators, or either of them with equal chance. Also, the dictators could avoid receiving the message at a price. We showed that pro-self dictators' willingness to pay to conceal messages decreased when information privacy diminished. Thus, results indicated that image concerns wane in an environment where information privacy is weak. Our results contribute to understanding of the privacy paradox.

Accuracy and diversity are two important aspects to evaluate the performance of recommender systems. Two diffusion-based methods were proposed respectively inspired by the mass diffusion (MD) and heat conduction (HC) processes on networks. It has been pointed out that MD has high recommendation accuracy yet low diversity, while HC succeeds in seeking out novel or niche items but with relatively low accuracy. The accuracy-diversity dilemma is a long-term challenge in recommender systems. To solve this problem, we introduced a background temperature by adding a ground user who connects to all the items in the user-item bipartite network. Performing the HC algorithm on the network with ground user (GHC), it showed that the accuracy can be largely improved while keeping the diversity. Furthermore, we proposed a weighted form of the ground user (WGHC) by assigning some weights to the newly added links between the ground user and the items. By turning the weight as a free parameter, an optimal value subject to the highest accuracy is obtained. Experimental results on three benchmark data sets showed that the WGHC outperforms the state-of-the-art method MD for both accuracy and diversity.