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. 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 com Personalization Sciences Yahoo Labs Sunnyvale CA 94089 USA ABSTRACT Many internet companies such as Yahoo Facebook Google and Twitter rely on content recommendation systems to deliver the most relevant content items to individual users through pe 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. 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? . (. Chapter 9) . Ken Koedinger. 1. Personalization Principle. Which is better for student learning?. Conversational style of instruction. Formal style of instruction. Example. : . “. You. should be very careful if . 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. 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. 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. 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.