PPT-Performance of Recommender Algorithms on Top-N Recommendati

Author : aaron | Published Date : 2017-06-26

Gabriel Vargas Carmona 220612 Agenda Introduction General Overview Recommender system Evaluation RMSE amp MAE Recall and precision Long tail Netflix and Movielens

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Performance of Recommender Algorithms on Top-N Recommendati: Transcript

Gabriel Vargas Carmona 220612 Agenda Introduction General Overview Recommender system Evaluation RMSE amp MAE Recall and precision Long tail Netflix and Movielens Collaborative . 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 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 Please read carefully as this brochure contains data vital to every stude right to attend Railway Bishop Ave Finch Ave Yonge St Bayview Ave Hwy 401 x the hydro electric power corridor on the north side of Finch Ave E between Yonge St and B ayview Av 1) New Paths to New Machine Learning Science. 2) How an Unruly Mob Almost Stole. the Grand Prize at the Last Moment. Jeff Howbert. February. . 6. , 2012. Netflix Viewing Recommendations. Recommender Systems. 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. 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. 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. 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 . Maureen Gallagher. SAM . International . conference . London, Oct . 17, 2013. Presentation Outline. Setting the context. Background. HSS & SAM treatment . Experiences – How does it look? . Strategic & practical implications. 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.,. 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.

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