PPT-Recommender Systems and Collaborative Filtering

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. 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. Agenda. Introduction. Charactarization of Attacks. Attack models. Effectivness analysis. Countermeasures. Privacy aspects. Discussion. Introduction / Background. (Monetary) value of being in recommendation lists. 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. 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. Alex Beutel. Joint work with Kenton Murray, . Christos . Faloutsos. , Alex . Smola. April 9, 2014 – Seoul, South Korea. Online Recommendation. 2. 5. Users. Movies. 5. 3. 5. 5. 2. Online Rating Models. 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. 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. Outline. Recap. SVD . vs. PCA. Collaborative filtering. aka Social recommendation. k-NN CF methods. classification. CF via MF. MF . vs. SGD . vs. ….. Dimensionality Reduction. and Principle Components Analysis: Recap.