Collaborative Filtering (CF) Recommender Systems (RSs) help users deal with the information overload they face when browsing, searching, or shopping for products and services. Power users are those individuals that are able to exert substantial influence over the recommendations made to other users, and RS operators encourage the existence of power user communities and leverage them to help fellow users make informed purchase decisions, especially on new items. Attacks on RSs occur when malicious users attempt to bias recommendations by introducing fake reviews or ratings; these attacks remain a key problem area for system operators. Thus, the influence wielded by power users can be used for both positive (addressing the "new item" problem) or negative (attack) purposes. Our research is investigating the impact on RS predictions and top-N recommendation lists when attackers emulate power users to provide biased ratings for new items. Previously we showed that power user attacks are effective against user-based CF RSs and that item-based CF RSs are robust to this type of attack. This paper presents the next stage in our investigation: (1) an evaluation of heuristic approaches to power user selection, and (2) evaluation of power user attacks in the context of matrix-factorization (SVD) based recommenders. Results show that social measures of influence such as degree centrality are more effective for selection of power users, and that matrix-factorization approaches are susceptible to power user attacks.

Public school choice at the primary and secondary levels is a keyelement of the U.S. No Child Left Behind Act of 2001 (NCLB). If aschool does not meet assessment goals for two consecutive years, bylaw the district must offer students the opportunity to transfer to aschool that is meeting its goals. Thus we have developed an online,content-based recommender system, called SmartChoice. Itprovides parents with school recommendations for individual studentsbased on parents' preferences and students' needs, interests,abilities, and talents.

Public school choice at the primary and secondary levels is a keyelement of the U.S. No Child Left Behind Act of 2001 (NCLB).  If aschool does not meet assessment goals for two consecutive years, bylaw the district must offer students the opportunity to transfer to aschool that is meeting its goals.  Making a choice with such potentialimpact on a child's future is clearly monumental, yet astonishinglyfew parents take advantage of the opportunity.  Our research has shownthat a significant part of the problem arises from issues ininformation access and information overload, particularly for lowsocioeconomic status families.  Thus we have developed an online,content-based recommender system, called SmartChoice.  Itprovides parents with school recommendations for individual studentsbased on parents' preferences and students' needs, interests,abilities, and talents.  The first version of the online applicationwas deployed and live for focus group participants who used it for theJanuary and March/April 2008 Charlotte-Mecklenburg school choiceperiods.  This article describes the SmartChoice Program and theresults of our initial and followup studies with participants.