PPT-Hierarchical Recommender System for Improving NPS.

Author : min-jolicoeur | Published Date : 2016-03-20

wwwkddunccedu CCI UNCCharlotte Research sponsored by p resented by Zbigniew W Ras CONSULTING COMPANY in Charlotte Client 1 Client 2 Client 3 Client 4 Build Recommender

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Hierarchical Recommender System for Improving NPS.: Transcript

wwwkddunccedu CCI UNCCharlotte Research sponsored by p resented by Zbigniew W Ras CONSULTING COMPANY in Charlotte Client 1 Client 2 Client 3 Client 4 Build Recommender System. 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 Levandoski Mohamed Sarwat Ahmed Eldawy Mohamed F Mokbel Microsoft Research Redmond WA USA Department of Computer Science and Engineering Universit y of Minnesota Minneapolis MN USA justinlevandoskimicrosoftcom sarwatcsumnedu eldawycsumnedu 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. News Recommender System Iv Interaction . Effectively, yet . Infrequently, Enables . Programmers to Discover New Tools. Emerson Murphy-Hill. North Carolina State University. Gail Murphy. University of British Columbia. 1. Background. This suggests that we need other ways to classify recommender algorithms. While a 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. 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. 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 . Classification of Transposable Elements . using a Machine . Learning Approach. Introduction. Transposable Elements (TEs) or jumping genes . are DNA . sequences that . have an intrinsic . capability to move within a host genome from one genomic location . 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 Produces a set of . nested clusters . organized as a hierarchical tree. Can be visualized as a . dendrogram. A tree-like diagram that records the sequences of merges or splits. Strengths of Hierarchical Clustering.

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