'Busy building recommender systems' presentation slideshows

Busy building recommender systems - PowerPoint PPT Presentation


Web 2.0 Expo San Francisco April 3, 2009

Web 2.0 Expo San Francisco April 3, 2009

Web 2.0 Expo San Francisco April 3, 2009. Gregor Hochmuth dotgrex.com / @grex. Finding and applying. INFLUENCE. Web 2.0 Expo San Francisco April 3, 2009. Gregor Hochmuth dotgrex.com. *. This is not a Google presentation.

By maxim
(108 views)

Web 2.0 Expo San Francisco April 3, 2009

Web 2.0 Expo San Francisco April 3, 2009

Web 2.0 Expo San Francisco April 3, 2009. Gregor Hochmuth dotgrex.com / @grex. Finding and applying. INFLUENCE. Web 2.0 Expo San Francisco April 3, 2009. Gregor Hochmuth dotgrex.com. *. This is not a Google presentation.

By trembley
(0 views)


View Busy building recommender systems PowerPoint (PPT) presentations online in SlideServe. SlideServe has a very huge collection of Busy building recommender systems PowerPoint presentations. You can view or download Busy building recommender systems presentations for your school assignment or business presentation. Browse for the presentations on every topic that you want.

Related Searches for Busy building recommender systems
Collaborative Recommender Systems for Building Automation

Collaborative Recommender Systems for Building Automation

Collaborative Recommender Systems for Building Automation. Michael LeMay, Jason J. Haas, and Carl A. Gunter University of Illinois. Overview. Motivation: Future Building Automation Systems (BASs) will support a wide variety of control algorithms

By emmanuel (157 views)

Recommender Systems

Recommender Systems

Recommender Systems. Finding Trusted Information. How many cows in Texas?. http://www.cowabduction.com/. Outline. What are Recommender Systems? How do they work? How can we integrate social information / trust? What are some applications?. Netflix. Amazon. How do they work? .

By nakishar (0 views)

Recommender Systems

Recommender Systems

Recommender Systems. Dr. Frank McCown Intro to Web Science Harding University. This work is licensed under a  Creative Commons Attribution- NonCommercial - ShareAlike 3.0 Unported License. Image: http://lifehacker.com/5642050/five-best-movie-recommendation-services. Recommender Systems.

By seiber (0 views)

Recommender systems

Recommender systems

Recommender systems. Drew Culbert IST 497 12/12/02. Overview. Definition Ways its used Problems Maintenance The future. What is it?. Recommender systems are a technological proxy for a social process.

By robertschultz (2 views)

Recommender Systems

Recommender Systems

Recommender Systems. Collaborative Filtering & Content-Based Recommending. Recommender Systems. Systems for recommending items (e.g. books, movies, CD ’ s, web pages, newsgroup messages) to users based on examples of their preferences.

By rosieg (0 views)

Recommender Systems

Recommender Systems

Recommender Systems. Collaborative Filtering & Content-Based Recommending Lecture 13. Recommender Systems. Systems for recommending items (e.g. books, movies, CD’s, web pages, newsgroup messages) to users based on examples of their preferences.

By leslieskinner (0 views)

Recommender systems

Recommender systems

Recommender systems. Ram Akella November 26 th 2008. Outline. Types of recommendation systems Search-based recommendations Category-based recommendations Collaborative filtering Clustering Association Rules Information filtering Classifiers. Types of recommendation systems.

By yonah (158 views)

Recommender Systems

Recommender Systems

Recommender Systems. >1,000,000,000. Finding Trusted Information. How many cows in Texas?. http://www.cowabduction.com/. Outline. What are Recommender Systems? How do they work? How can we integrate social information / trust? What are some applications?. Netflix. Amazon.

By rashad-adams (208 views)

Recommender Systems

Recommender Systems

Recommender Systems. Collaborative Filtering & Content-Based Recommending. Recommender Systems. Systems for recommending items (e.g. books, movies, CD’s, web pages, newsgroup messages) to users based on examples of their preferences.

By raja-mcgee (191 views)