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Applied and Computational Mathematics Seminar

Applied Math Seminar: Master's Talk

Title:   Matrix Factorization Techniques for Recommender Systems

Abstract: Recommendation Systems apply Information Retrieval techniques to select the online information relevant to a given user. Collaborative Filtering (CF) is currently most widely used approach to build Recommendation System. To address this issue, the collaborative filtering recommendation algorithm is based on singular value decomposition (SVD) . How the SVD works to make recommendations is presented in this master talk.

Date:
-
Location:
POT 110

Applied Math Seminar: Master's Talk

Title:   Matrix Factorization Techniques for Recommender Systems

Abstract: Recommendation Systems apply Information Retrieval techniques to select the online information relevant to a given user. Collaborative Filtering (CF) is currently most widely used approach to build Recommendation System. To address this issue, the collaborative filtering recommendation algorithm is based on singular value decomposition (SVD) . How the SVD works to make recommendations is presented in this master talk.

Date:
-
Location:
POT 110

Applied Math Seminar: Master's Talk

Title:   Matrix Factorization Techniques for Recommender Systems

Abstract: Recommendation Systems apply Information Retrieval techniques to select the online information relevant to a given user. Collaborative Filtering (CF) is currently most widely used approach to build Recommendation System. To address this issue, the collaborative filtering recommendation algorithm is based on singular value decomposition (SVD) . How the SVD works to make recommendations is presented in this master talk.

Date:
-
Location:
POT 110

Applied Math Seminar: Master's Talk

Title:   Matrix Factorization Techniques for Recommender Systems

Abstract: Recommendation Systems apply Information Retrieval techniques to select the online information relevant to a given user. Collaborative Filtering (CF) is currently most widely used approach to build Recommendation System. To address this issue, the collaborative filtering recommendation algorithm is based on singular value decomposition (SVD) . How the SVD works to make recommendations is presented in this master talk.

Date:
-
Location:
POT 110

Applied Math Seminar: Master's Talk

Jonathan Proctor will be giving a Master's Talk.  He will be presenting the paper

SIAM Rev., 52(1), 3–54. (52 pages)
Numerical Methods for Electronic Structure Calculations of Materials

 

Date:
-
Location:
POT 745

Applied Math Seminar: Master's Talk

Jonathan Proctor will be giving a Master's Talk.  He will be presenting the paper

SIAM Rev., 52(1), 3–54. (52 pages)
Numerical Methods for Electronic Structure Calculations of Materials

 

Date:
-
Location:
POT 745

Applied Math Seminar: Master's Talk

Jonathan Proctor will be giving a Master's Talk.  He will be presenting the paper

SIAM Rev., 52(1), 3–54. (52 pages)
Numerical Methods for Electronic Structure Calculations of Materials

 

Date:
-
Location:
POT 745

Applied Math Seminar: Master's Talk

Jonathan Proctor will be giving a Master's Talk.  He will be presenting the paper

SIAM Rev., 52(1), 3–54. (52 pages)
Numerical Methods for Electronic Structure Calculations of Materials

 

Date:
-
Location:
POT 745

Applied Math Seminar: Master's Talk

Jonathan Proctor will be giving a Master's Talk.  He will be presenting the paper

SIAM Rev., 52(1), 3–54. (52 pages)
Numerical Methods for Electronic Structure Calculations of Materials

 

Date:
-
Location:
POT 745

Applied Math Seminar: Master's Talk

Jonathan Proctor will be giving a Master's Talk.  He will be presenting the paper

SIAM Rev., 52(1), 3–54. (52 pages)
Numerical Methods for Electronic Structure Calculations of Materials

 

Date:
-
Location:
POT 745