Chongjing Sun

Ya-Hui An, Qiang Dong, Chong-Jing Sun et al.

In last decades, diversity and accuracy have been regarded as two important measures in evaluating a recommendation model. However, a clear concern is that a model focusing excessively on one measure will put the other one at risk, thus it is not easy to greatly improve diversity and accuracy simultaneously. In this paper, we propose to enhance the Resource-Allocation (RA) similarity in resource transfer equations of diffusion-like models, by giving a tunable exponent to the RA similarity, and traversing the value of the exponent to achieve the optimal recommendation results. In this way, we can increase the recommendation scores (allocated resource) of many unpopular objects. Experiments on three benchmark data sets, MovieLens, Netflix, and RateYourMusic show that the modified models can yield remarkable performance improvement compared with the original ones.

Chongjing Sun, Philip S. Yu, Xiangnan Kong et al.

Publishing social network data for research purposes has raised serious concerns for individual privacy. There exist many privacy-preserving works that can deal with different attack models. In this paper, we introduce a novel privacy attack model and refer it as a mutual friend attack. In this model, the adversary can re-identify a pair of friends by using their number of mutual friends. To address this issue, we propose a new anonymity concept, called k-NMF anonymity, i.e., k-anonymity on the number of mutual friends, which ensures that there exist at least k-1 other friend pairs in the graph that share the same number of mutual friends. We devise algorithms to achieve the k-NMF anonymity while preserving the original vertex set in the sense that we allow the occasional addition but no deletion of vertices. Further we give an algorithm to ensure the k-degree anonymity in addition to the k-NMF anonymity. The experimental results on real-word datasets demonstrate that our approach can preserve the privacy and utility of social networks effectively against mutual friend attacks.