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. An Adaptive Framework for Similarity Join and Search. Jiannan. Wang. . (Tsinghua University). Guoliang. . Li (Tsinghua . University). Jianhua. . Feng. (Tsinghua University). Data Integration. Data Cleaning. 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?. 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? . Agenda. Collaborative Filtering (CF). Pure CF approaches. User-based nearest-neighbor. The Pearson Correlation similarity measure. Memory-based and model-based approaches. Item-based nearest-neighbor. 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. CS5670: Intro to Computer Vision. Noah Snavely. Hybrid Images, . Oliva. et al., . http://cvcl.mit.edu/hybridimage.htm. Lecture 1: Images and image filtering. Noah Snavely. Hybrid Images, . Oliva. et al., . 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. Atif. . Iqbal. . Thesis Overview. 2. Introduction. Motivation. Previous Works. Cascaded Filtering for . Palmprints. Cascaded Filtering . for Fingerprints. Summary and Conclusion. What is Biometrics?.