PPT-Performance of Recommender Algorithms on Top-N Recommendation Tasks

Author : debby-jeon | Published Date : 2020-01-06

Performance of Recommender Algorithms on TopN Recommendation Tasks Gabriel Vargas Carmona 220612 Agenda Introduction General Overview Recommender system Evaluation

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Performance of Recommender Algorithms on Top-N Recommendation Tasks: Transcript

Performance of Recommender Algorithms on TopN Recommendation Tasks Gabriel Vargas Carmona 220612 Agenda Introduction General Overview Recommender system Evaluation RMSE amp MAE Recall and precision. 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?. Describing what you know. Contents. What are they and were do we find them?. Why show the algorithm?. What formalisms are used for presenting algorithms?. Notes on notation. Algorithmic performance. Where do we find them. 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. Online consumer decision making. Introduction. Context effects. Primacy/. recency. effects. Further effects. Personality and social psychology. Discussion and . summary. Literature. Introduction. 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. 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 . SESSION 2:. . Tasks . in . IDM. Summative. Performance Tasks. Formative Performance . Tasks. Summative . extensions. Taking informed . a. ction. If students are asked a . COMPELLING QUESTION. …. 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. © 2016 The Common . Application. • . commonapp.org. RECOMMENDATION. PROCESS. AGENDA. FERPA Release Authorization. Invite & Assign Recommenders. Status & Management. © 2016 The Common Application. 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.

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