PPT-Secure Personalization Building Trustworthy recommender systems

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 BUSINESS WIRE Conversant Inc NASDAQCNVR the leader in personalized digital marketing today announced that it has appointed Raju Malhotra as senior vice president of Products In this role Mr Malhotra will drive a unified product strategy across the 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. Explanations in recommender systems. Motivation. “The . digital camera . Profishot. . is a must-buy for you because . . . . .”. Why should recommender systems deal . with explanations at . all?. Hybrid recommender systems. Hybrid: combinations of various inputs and/or composition of different mechanism. Knowledge-based: "Tell me what fits based on my needs". Content-based: "Show me more of the same what I've liked. A . B. uzz . W. ord . or the . Key . to . Future . S. uccess . for G. rocers?. FMI Connect Webinar. . – . June 9. th. , 2016. Today’s Presenter. Graeme . McVie. VP . & . GM. Business Development. 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. Chapin . Brinegar. MIT511. Introduction. Reading, viewing a presentation or playing an interactive game are . social. events.. Implied conversation between author and learner(s). Words, Voice and Animation are important!. Loyal. Helpful. Friendly. Courteous. Kind. Obedient. Cheerful. Thrifty. Brave. Clean. Reverent. Trustworthy. Loyal. Helpful. Friendly. Courteous. Kind. Obedient. Cheerful. Thrifty. Brave. Clean. Reverent. Is the Bible trustworthy? . 2. UCCF Doctrinal Basis. c) The Bible, as originally given, is the inspired and infallible Word of God. It is the supreme authority in all matters of belief and behaviour.. Christo Wilson. Assistant Professor @ Northeastern University. cbw@ccs.neu.edu. Personalization on the Web. Santa Barbara, California. Amherst, Massachusetts. Personalization is Ubiquitous. Search Results. 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. Reaching Students of Generation Z. Scott Burke, AVP of Undergraduate Admissions, Georgia State University. Ryan Hogan, Director of Admissions, Valdosta State University. Joel Lee, Assistant Vice Chancellor for Enrollment Management , Winston-Salem State University .