Xiao-Long Ren

Leilei Wu, Zhuoming Ren, Xiao-Long Ren et al.

The ongoing rapid development of the e-commercial and interest-base websites make it more pressing to evaluate objects' accurate quality before recommendation by employing an effective reputation system. The objects' quality are often calculated based on their historical information, such as selected records or rating scores, to help visitors to make decisions before watching, reading or buying. Usually high quality products obtain a higher average ratings than low quality products regardless of rating biases or errors. However many empirical cases demonstrate that consumers may be misled by rating scores added by unreliable users or deliberate tampering. In this case, users' reputation, i.e., the ability to rating trustily and precisely, make a big difference during the evaluating process. Thus, one of the main challenges in designing reputation systems is eliminating the effects of users' rating bias on the evaluation results. To give an objective evaluation of each user's reputation and uncover an object's intrinsic quality, we propose an iterative balance (IB) method to correct users' rating biases. Experiments on two online video-provided Web sites, namely MovieLens and Netflix datasets, show that the IB method is a highly self-consistent and robust algorithm and it can accurately quantify movies' actual quality and users' stability of rating. Compared with existing methods, the IB method has higher ability to find the "dark horses", i.e., not so popular yet good movies, in the Academy Awards.

Xiaofang Deng, Leilei Wu, Xiaolong Ren et al.

Recommender systems, inferring users' preferences from their historical activities and personal profiles, have been an enormous success in the last several years. Most of the existing works are based on the similarities of users, objects or both that derived from their purchases records in the online shopping platforms. Such approaches, however, are facing bottlenecks when the known information is limited. The extreme case is how to recommend products to new users, namely the so-called cold-start problem. The rise of the online social networks gives us a chance to break the glass ceiling. Birds of a feather flock together. Close friends may have similar hidden pattern of selecting products and the advices from friends are more trustworthy. In this paper, we integrate the individual's social relationships into recommender systems and propose a new method, called Social Mass Diffusion (SMD), based on a mass diffusion process in the combined network of users' social network and user-item bipartite network. The results show that the SMD algorithm can achieve higher recommendation accuracy than the Mass Diffusion (MD) purely on the bipartite network. Especially, the improvement is striking for small degree users. Moreover, SMD provides a good solution to the cold-start problem. The recommendation accuracy for new users significantly higher than that of the conventional popularity-based algorithm. These results may shed some light on the new designs of better personalized recommender systems and information services.

Xiao-Long Ren, Niels Gleinig, Dijana Tolic et al.

The robustness of complex networks under targeted attacks is deeply connected to the resilience of complex systems, i.e., the ability to make appropriate responses to the attacks. In this article, we investigated the state-of-the-art targeted node attack algorithms and demonstrate that they become very inefficient when the cost of the attack is taken into consideration. In this paper, we made explicit assumption that the cost of removing a node is proportional to the number of adjacent links that are removed, i.e., higher degree nodes have higher cost. Finally, for the case when it is possible to attack links, we propose a simple and efficient edge removal strategy named Hierarchical Power Iterative Normalized cut (HPI-Ncut).The results on real and artificial networks show that the HPI-Ncut algorithm outperforms all the node removal and link removal attack algorithms when the cost of the attack is taken into consideration. In addition, we show that on sparse networks, the complexity of this hierarchical power iteration edge removal algorithm is only $O(n\log^{2+ε}(n))$.