NEW ALGORITHM FOR TENSOR BASED RECOMMENDATION SYSTEM

Q: How can I download an article?

To download an article from SID, first log in to the site, search for the article title, and click on the 'Download Article' option.

Q: How can I download an ISI article?

To download an ISI article on SID, enter the keyword or article title in the search bar, view the relevant results, click on the desired article, and select the 'Download Article' option.

Q: How can I access the SID database?

To access the SID database, visit SID.ir, create an account, and log in to access scientific resources.

Q: Is downloading articles from SID free?

Some articles on SID are available for free, while others require payment. Details are specified on the article's page.

NASIRI M.; REZGHI S.M.; MINAEI B

Scientific Information Database (SID) - Trusted Source for Research and Academic Resources

Journal Paper

Paper Information

Journal: Journal of Operational Research in its Applications Year:2014 | Volume:11 | Issue:1 (40) Page(s): 57-64

Download Full-Text

مرکز اطلاعات علمی Scientific Information Database (SID) - Trusted Source for Research and Academic Resources

Persian Verion

View:

1,583

Download:

Cites:

Information Journal Paper

Title

NEW ALGORITHM FOR TENSOR BASED RECOMMENDATION SYSTEM

Author(s)

NASIRI M. | REZGHI S.M. | MINAEI B | Issue Writer Certificate

Keywords

RECOMMENDATION SYSTEMQ2

TENSOR DECOMPOSITIONQ2

SPARSE DATAQ2

DYNAMIC RECOMMENDATIONQ2

Abstract

In recent years, use of RECOMMENDATION SYSTEMs has been more. Existing challenges in RECOMMENDATION SYSTEM datasets are interesting for researchers. Using matrix decompositions and then TENSOR DECOMPOSITIONs in RECOMMENDATION SYSTEM algorithms have less error and results in that most of new algorithms use them. In this paper we present a new algorithm that uses TENSOR DECOMPOSITION and have three dimensions user-item-time. Our algorithm has better result in comparison with matrix decomposition with 2 dimension user-item. In this method we use time as an independent dimension and 3rd dimension. Experiments show that using time in decompositions has less error.

Cites

No record.

References

MAKE NEW FRIENDS, BUT KEEP THE OLD RECOMMENDING PEOPLE ON SOCIAL NETWORKING SITES

Cite

APA: Copy

NASIRI, M., REZGHI, S.M., & MINAEI, B. (2014). NEW ALGORITHM FOR TENSOR BASED RECOMMENDATION SYSTEM. JOURNAL OF OPERATIONAL RESEARCH AND ITS APPLICATIONS (JOURNAL OF APPLIED MATHEMATICS), 11(1 (40)), 57-64. SID. https://sid.ir/paper/164377/en

Vancouver: Copy

NASIRI M., REZGHI S.M., MINAEI B. NEW ALGORITHM FOR TENSOR BASED RECOMMENDATION SYSTEM. JOURNAL OF OPERATIONAL RESEARCH AND ITS APPLICATIONS (JOURNAL OF APPLIED MATHEMATICS)[Internet]. 2014;11(1 (40)):57-64. Available from: https://sid.ir/paper/164377/en

IEEE: Copy

M. NASIRI, S.M. REZGHI, and B MINAEI, “NEW ALGORITHM FOR TENSOR BASED RECOMMENDATION SYSTEM,” JOURNAL OF OPERATIONAL RESEARCH AND ITS APPLICATIONS (JOURNAL OF APPLIED MATHEMATICS), vol. 11, no. 1 (40), pp. 57–64, 2014, [Online]. Available: https://sid.ir/paper/164377/en

Related Journal Papers