PPT-User-friendly NPS-based recommender system for driving business revenue.

Author : marina-yarberry | Published Date : 2018-09-25

CCI UNCCharlotte Research sponsored by presented by Zbigniew W Ras WI17 Leipzig Germany Project Team Kasia Tarnowska Warsaw Univ of Tech Poland Pauline Brunet

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User-friendly NPS-based recommender system for driving business revenue.: Transcript

CCI UNCCharlotte Research sponsored by presented by Zbigniew W Ras WI17 Leipzig Germany Project Team Kasia Tarnowska Warsaw Univ of Tech Poland Pauline Brunet Paris Tech. 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. 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. Fuzzy. FCA. ICFCA 2013. Dresden, Germany. Juraj. . Macko. , . Palacky. University, Olomouc, Czech Republic. User-Friendly . Fuzzy. FCA. is oxymoron.. Isn’t it? . . User-Friendly . 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. Danielle Lee . April 20, 2011. Three basic recommendations . Collaborative Filtering. : exploiting other likely-minded community data to derive recommendations. Effective, Novel and Serendipitous recommendations . Gabriel Vargas Carmona. 22.06.12. Agenda. Introduction. General Overview. Recommender. . system. Evaluation. RMSE & MAE. Recall . and. . precision. Long-. tail. Netflix. . and. . Movielens. Collaborative . 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. S. OCIAL. N. ETWORKS. Modified from . R. . . Zafarani. , M. A. . Abbasi. , and H. Liu, . Social Networks . Mining: An Introduction. , Cambridge University Press, 2014. . Difficulties of Decision Making. 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. S. OCIAL. N. ETWORKS. Modified from . R. . . Zafarani. , M. A. . Abbasi. , and H. Liu, . Social Networks . Mining: An Introduction. , Cambridge University Press, 2014. . Difficulties of Decision Making. Performance of Recommender Algorithms on Top-N Recommendation Tasks Gabriel Vargas Carmona 22.06.12 Agenda Introduction General Overview Recommender system Evaluation RMSE & MAE Recall and precision 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.

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