We present a new approach to help make computer science classes both more social and more effective: "lightweight teams". Lightweight teams are class teams in which the team members have little or no direct impact on each other's final grades, yet where there is a significant component of peer teaching, peer learning and long-term socialization built into the curriculum. We explain how lightweight teams have been used in a CS1 class at our institution, and how this approach, combined with a flipped class approach and gamification, has led to high levels of student engagement, despite the difficulty of the material and the frustration that is common to those first learning to program.
Recommender Systems (RS) are vulnerable to attack by malicious users who intend to bias the recommendations for their own benefit. Research in this area has developed attack models, detection methods, and mitigation schemes to understand and protect against such attacks. For Collaborative Filtering RSs, model-based approaches such as item-based and matrix-factorization were found to be more robust to many types of attack. Advice in designing for system robustness has thus been to employ model-based approaches. Our recent work with the Power User Attack (PUA), however, determined that attackers disguised as influential users can successfully attack (from the attacker's viewpoint) SVD-based recommenders, as well as user-based. But item-based systems remained robust to the PUA. In this paper we investigate a new, complementary attack model, the Power Item Attack (PIA), that uses influential items to successfully attack RSs. We show that the PIA is able to impact not only user-based and SVD-based recommenders but also the heretofore highly robust item-based approach, using a novel multi-target attack vector.
Power users, in a Collaborative Filtering (CF) Recommender System (RS) context, are those who can exert considerable influence over the recommendation outcomes presented to other users. RS operators encourage the existence of power user communities and leverage them to help fellow users make informed purchase decisions. Thus, RS research in this area has focused on power user selection and utilization to address challenges such as rating sparsity for new items or users. But, as ever, there remains the potential for corruption by power users who provide biased ratings. And because of the influence that power users wield, biased ratings they provide can have significant impacts on RS accuracy and robustness. In order to better understand this problem and develop solution strategies, our research is investigating the impact on RS predictions and top-N recommendation lists when power users provide biased ratings. The open areas of research we have explored are analyzing and evaluating power user selection techniques, statistically characterizing power users in order to create attack profiles, mounting power user attacks on new items, and using accuracy and robustness metrics to evaluate power user attacks. In the future, we plan to extend our initial research in power user selection, characterization, and evaluation, as well as generate attack profiles based on power user characteristics, mount power user attacks on user-based, itembased, and SVD-based CF systems, evaluate power user attacks, and generalize our work across different domains.
Power users, in a Collaborative Filtering (CF) Recommender System (RS) context, are those who can exert considerable influence over the recommendation outcomes presented to other users. RS operators encourage the existence of power user communities and leverage them to help fellow users make informed purchase decisions. Thus, RS research in this area has focused on power user identification and utilization to address challenges such as rating sparsity for new items or users. But, as ever, there remains the potential for corruption of power. Alongside accuracy and efficiency measures, RS robustness to manipulation or 'attack' has been studied using injection of false user profiles. Our research is investigating the impact on RS predictions and top-N recommendation lists when simulated power users provide biased ratings for new items. In this study, we introduce the notion of a 'Power User Attack' for RS robustness analysis, as well as a novel use of social networking degree centrality concepts for identifying RS power users. Initial results show that power users identified using in-degree centrality, compared to other techniques, can be more influential as reflected by accuracy and robustness impacts before and after power user attacks.
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