This is our experiments codes for the paper:

Han Liu, Xiangnan He, Fuli Feng, Liqiang Nie, Rui Liu, Hanwang Zhang (2018). Discrete Factorization Machines for Fast Feature-based Recommendation. In Proceedings of IJCAI'18.

For any issue, please contact Han Liu: hanliu.sdu@gmail.com

MATLAB R2016a.

Please run the 'test.m' file by inputting the command below in MATLAB command window, then the training and the testing of DFM will automatically proceed.

Run DFM:

test

During the training process, the value of loss function and objective function will be printed in MATLAB command window after each optimization iteration.

Output (training process):

DFM at bit 64 Iteration:20
loss value = 1109405916.9047 obj value = 1153073221.0511

After the testing process, the NDCG(NDCG@1 to NDCG@10) of DFM on the testing set will be printed in MATLAB command window.

Output (testing process):

The DFM ndcg@1 is 0.81726
The DFM ndcg@2 is 0.812
The DFM ndcg@3 is 0.81073
The DFM ndcg@4 is 0.81256
The DFM ndcg@5 is 0.81639
The DFM ndcg@6 is 0.82228
The DFM ndcg@7 is 0.83002
The DFM ndcg@8 is 0.83914
The DFM ndcg@9 is 0.84877
The DFM ndcg@10 is 0.85742

We provide two processed datasets: Yelp and Amazon.

train_yelp, train_amazon:

• Train file.
• Each Line is a training instance: userID\itemID\rating

test_yelp, test_amazon:

• Test file.
• Each Line is a testing instance: userID\itemID\rating

feature_yelp, feature_amazon:

• Feature file.
• Each Line is a item's content-based information vector.