Discrete Factorization Machines
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: email@example.com
Example to run the codes.
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.
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 file.
- Each Line is a training instance: userID\itemID\rating
- Test file.
- Each Line is a testing instance: userID\itemID\rating
- Feature file.
- Each Line is a item's content-based information vector.