Predictability Issues in Recommender Systems Based on Web Usage Behavior towards Robust Collaborative Filtering

This paper examines the effect of Recommender Systems in security oriented issues. Currently research has begun to evaluate the vulnerabilities and robustness of various collaborative recommender techniques in the face of profile injection and shilling attacks. Standard collaborative filtering algorithms are vulnerable to attacks. The robustness of recommender system and the impact of attacks are well suited this study and examined in this paper. The predictability issues and the various attack strategies are also discussed. Based on KNN the robustness of the recommender system were examined and the sensitivity of the rating given by the users are also analyzed. Furthermore the robust PLSA also considered for the work

Incentive-Centered Design for User-Contributed Content

We review incentive-centered design for user-contributed content (UCC) on the Internet. UCC systems, produced (in part) through voluntary contributions made by non-employees, face fundamental incentives problems. In particular, to succeed, users need to be motivated to contribute in the first place ("getting stuff in"). Further, given heterogeneity in content quality and variety, the degree of success will depend on incentives to contribute a desirable mix of quality and variety ("getting \emph{good} stuff in"). Third, because UCC systems generally function as open-access publishing platforms, there is a need to prevent or reduce the amount of negative value (polluting or manipulating) content. The work to date on incentives problems facing UCC is limited and uneven in coverage. Much of the empirical research concerns specific settings and does not provide readily generalizable results. And, although there are well-developed theoretical literatures on, for example, the private provision of public goods (the "getting stuff in" problem), this literature is only applicable to UCC in a limited way because it focuses on contributions of (homogeneous) money, and thus does not address the many problems associated with heterogeneous information content contributions (the "getting \emph{good} stuff in" problem). We believe that our review of the literature has identified more open questions for research than it has pointed to known results.http://deepblue.lib.umich.edu/bitstream/2027.42/100229/1/icd4ucc.pdf7