PPT-Attacks on collaborative recommender systems

Agenda Introduction Charactarization of Attacks Attack models Effectivness analysis Countermeasures Privacy aspects Discussion Introduction Background Monetary value of being in recommendation lists. In57357uenc is measure of the e57355ect of user on the recommendations from recommender system In 57357uence is erful to ol for understanding the orkings of recommender system Exp erimen ts sho that users ha widely arying degrees of in57357uence in www.kdd.uncc.edu. CCI, UNC-Charlotte. Research sponsored . by:. p. resented by. Zbigniew. W. Ras. CONSULTING COMPANY in Charlotte. Client 1. Client 2. Client 3. Client 4. Build . Recommender System. Dietmar. . Jannach. , Markus . Zanker. , Alexander . Felfernig. , Gerhard Friedrich. Cambridge University Press. Which digital camera should I buy. ?. What is the best holiday for me and. my family. Evaluating Recommender Systems. A myriad of techniques has been proposed, . but. Which one is the best in a given application domain?. What are the success factors of different techniques?. Comparative analysis based on an optimality criterion? . Agenda. Online consumer decision making. Introduction. Context effects. Primacy/. recency. effects. Further effects. Personality and social psychology. Discussion and . summary. Literature. Introduction. Dr. Frank McCown. Intro to Web Science. Harding University. This work is licensed under Creative . Commons . Attribution-. NonCommercial. . 3.0. Image: . http://lifehacker.com/5642050/five-best-movie-recommendation-services. Gabriel Vargas Carmona. 22.06.12. Agenda. Introduction. General Overview. Recommender. . system. Evaluation. RMSE & MAE. Recall . and. . precision. Long-. tail. Netflix. . and. . Movielens. Collaborative . and. Collaborative Filtering. 1. Matt Gormley. Lecture . 26. November 30, 2016. School of Computer Science. Readings:. Koren. et al. (2009). Gemulla. et al. (2011). 10-601B Introduction to Machine Learning. Charles Heckscher. August, . 2017. 1. CRAFT / AUTONOMOUS PROFESSIONAL NETWORKS. Customization and personal relations. Challenge: to increase scale of production and scope of distribution. 1900-. 1980. in the Presence of Adversaries?. Bamshad Mobasher. Center for Web Intelligence. School of Computing, DePaul University, Chicago, Illinois, USA. Personalization / Recommendation Problem. Dynamically serve customized content (pages, products, recommendations, etc.) to users based on their profiles, preferences, or expected interests. Performance of Recommender Algorithms on Top-N Recommendation Tasks Gabriel Vargas Carmona 22.06.12 Agenda Introduction General Overview Recommender system Evaluation RMSE & MAE Recall and precision Characterizing collaborative/coordinated attacks. Types of collaborative attacks. Identifying Malicious activity. Identifying Collaborative Attack. . . 3. Collaborative Attacks. Informal definition:. Introduction to Recommender Systems. Recommender systems: The task. Customer W. 2. Slides adapted from Jure Leskovec. Plays an Ella Fitzgerald song. What should we recommend next?. Thomas . Quella. Wikimedia Commons. Bharat Bhargava. CERIAS and CS department. Purdue University. www.cs.purdue.edu/homes/bb. 1. Acknowledgement. Thanks to all my sponsors in Motorola, Northrup Grumman corporation, Air Force. Thanks to my students.