Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Prettify README format by adding \n #41

Open
wants to merge 1 commit into
base: master
Choose a base branch
from
Open

Prettify README format by adding \n #41

Changes from all commits
Commits
Show all changes
1 commit
Select commit
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
1 change: 1 addition & 0 deletions README.md
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -3,6 +3,7 @@
This is a python implementation of Factorization Machines [1]. This uses stochastic gradient descent with adaptive regularization as a learning method, which adapts the regularization automatically while training the model parameters. See [2] for details. From libfm.org: "Factorization machines (FM) are a generic approach that allows to mimic most factorization models by feature engineering. This way, factorization machines combine the generality of feature engineering with the superiority of factorization models in estimating interactions between categorical variables of large domain."

[1] Steffen Rendle (2012): Factorization Machines with libFM, in ACM Trans. Intell. Syst. Technol., 3(3), May.

[2] Steffen Rendle: Learning recommender systems with adaptive regularization. WSDM 2012: 133-142

## Installation
Expand Down