PPT-Personalization & Recommender Systems

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. 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 Deepak . Agarwal. Yahoo! Research. Yucheng Low . Carnegie Mellon University. Alexander J. . Smola. Yahoo! Research. Information Flood. Personalization. 3. Golf Reader. Tech. Reader. Can we provide personalization to new users?. . . MARKETING TECHNOLOGY. . SIMPLIFIED.. WE KNOW THE IMPACT OF WEB . IT’S ONLY STARTING . LET’S DRILL DOWN ON AUTOMOTIVE SALES AND MARKETING … . “Mind the consideration gap”. AND PERSONALISATION. 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?. 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? . 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. 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 . 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!. 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. CCI, UNC-Charlotte. Research sponsored by. presented by. Zbigniew. W. Ras. WI’17, Leipzig, Germany. Project Team. Kasia. . Tarnowska. (Warsaw Univ. of . Tech., . Poland. ). Pauline Brunet. (Paris Tech.,. 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. Quick tips: Principles. Simon Kingsnorth. Author of Digital Marketing Strategy: An integrated approach to digital marketing. User Experience. Ensure your roles are defined clearly. Who owns UX, design, business requirements?.