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 Collaborative 64257ltering the most success ful recommendation approach makes recommendations based on past transactions and feedback from consumers sharing similar interests A major problem limiting the usefulness of collaborative 64257ltering is t H. Munoz-Avila. Case-Based Reasoning. Example: Slide Creation. Repository of Presentations:. 5/9/00: ONR review. 8/20/00: EWCBR talk. 4/25/01: DARPA review. Specification. Revised. talk . 3. . Revise. e-Commerce and Life Style Informatics: . Recommender Systems I. February 4 2013. Geoffrey Fox. gcf@indiana.edu. . . http://. www.infomall.org/X-InformaticsSpring2013/index.html. . Associate Dean for Research and Graduate Studies,  School of Informatics and Computing. Problem formulation. Machine Learning. Example: Predicting movie ratings. User rates movies using one to five stars. Movie. Alice (1). Bob (2). Carol (3). Dave (4). Love at last. Romance forever. Cute puppies of love. 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. Bamshad Mobasher. DePaul University. 2. What Is Prediction?. Prediction is similar to classification. First, construct a model. Second, use model to predict unknown value. Prediction is different from classification. 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. Evaluation. Tokenization and properties of text . Web crawling. Query models. Vector methods. Measures of similarity. Indexing. Inverted files. Basics of internet and web. Spam and SEO. Search engine design. Evaluation. Tokenization and properties of text . Web crawling. Query models. Vector methods. Measures of similarity. Indexing. Inverted files. Basics of internet and web. Spam and SEO. Search engine design. Characterizing collaborative/coordinated attacks. Types of collaborative attacks. Identifying Malicious activity. Identifying Collaborative Attack. . . 3. Collaborative Attacks. Informal definition:. Bharat Bhargava. . . 2. Trusted Router and Protection Against Collaborative Attacks. Characterizing collaborative/coordinated attacks. Types of collaborative attacks. Identifying Malicious activity. 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.