PPT-Recommender Systems – An Introduction

Author : alida-meadow | Published Date : 2016-05-25

Dietmar Jannach Markus Zanker Alexander Felfernig Gerhard Friedrich Cambridge University Press Which digital camera should I buy What is the best holiday

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Recommender Systems – An Introduction: Transcript

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. This paper also describes various limitations of current recommendation methods and discusses possible extensions that can improve recommendation capabilities and make recommender systems applicable to an even broader range of applications These ext umnedu GroupLens Research Group Army HPC Research Center Department of Computer Science and Engineering University of Minnesota Minneapolis MN 55455 USA Abstract We investigate the use of dimensionality reduction to improve the performance for a new Oard and Jinmook Kim Digital Library Research Group College of Library and Information Services University of Maryland College Park MD 20742 oard jinmookglueumdedu Abstract Can implicit feedback substitute for explicit ratings in re commender system 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 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. Agenda. Online consumer decision making. Introduction. Context effects. Primacy/. recency. effects. Further effects. Personality and social psychology. Discussion and . summary. Literature. Introduction. 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. Gabriel Vargas Carmona. 22.06.12. Agenda. Introduction. General Overview. Recommender. . system. Evaluation. RMSE & MAE. Recall . and. . precision. Long-. tail. Netflix. . and. . Movielens. Collaborative . www.kdd.uncc.edu. CCI, UNC-Charlotte. Research sponsored . by:. p. resented by. Zbigniew. W. Ras. CONSULTING COMPANY in Charlotte. Client 1. Client 2. Client 3. Client 4. Build . Recommender System. 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. 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.,. Infrequently, Enables . Programmers to Discover New Tools. Emerson Murphy-Hill. North Carolina State University. Gail Murphy. University of British Columbia. 1. Background. Emerson’s Problem. I was making a bunch of new user interfaces for . 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

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