PPT-Evaluating Recommender Systems

Author : luanne-stotts | Published Date : 2016-06-21

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

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Evaluating Recommender Systems: Transcript

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 . In evaluating the relevance of a programme or a project it is useful to consider the following questions To what extent are the objectives of the programme still valid Are the activities and outputs of the programme consistent with the overall goal 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. Adam Crymble. Plan for Today. Background . lecture. Discussion of experience with digital archives. Workshop: evaluating a historical website. Time to start drafting blog post. Terminology. Archive. Evaluating Sources. 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. 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. 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. in the Presence of Adversaries?. Bamshad Mobasher. Center for Web Intelligence. School of Computing, DePaul University, Chicago, Illinois, USA. Personalization / Recommendation Problem. Dynamically serve customized content (pages, products, recommendations, etc.) to users based on their profiles, preferences, or expected interests. 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. 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.,. 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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