PPT-Secure Personalization Building Trustworthy recommender systems

Author : yoshiko-marsland | Published Date : 2018-03-14

in the Presence of Adversaries Bamshad Mobasher Center for Web Intelligence School of Computing DePaul University Chicago Illinois USA Personalization Recommendation

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Secure Personalization Building Trustworthy recommender systems: Transcript

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. 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 White Paper Cisco Visual Networking Index: Forecast and Methodology, 20112016 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. Ted . Huffmire. ACACES 2012. Fiuggi. , Italy. Disclaimer. The views presented in this course are those of the speaker and do not necessarily reflect the views of the United States Department of Defense.. 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. Enhanced System Security and Quality not only provides plain printing technology to its customers but sets world class standards when it comes to laser printing quality 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. Introduction. Jan . 8, 2013. IS 2620. James Joshi, . Associate Professor. Contact. James Joshi. 706A, IS Building. Phone: 412-624-9982 . E-mail: . jjoshi@mail.sis.pitt.edu. Web: . http://www.sis.pitt.edu/~jjoshi/courses/IS2620/Spring13/. 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. 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. Bamshad Mobasher. Center for Web Intelligence. DePaul . University, Chicago, Illinois, USA. Predictive User Modeling for Personalization. The Problem. Dynamically serve customized content (ads, products, deals, recommendations, etc.) to users based on their profiles, preferences, or expected needs. Building Trustworthy, Secure Systems for the United States Critical Infrastructure An Urgent National Imperative The Current Landscape. It’s a dangerous world in cyberspace… Cyber Risk. Function The Desired Brand Effect Stand Out in a Saturated Market with a Timeless Brand

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