Adaptive User Modeling with Long and Short-Term Preference for Personalized Recommendation
This code provides an implementation of the SLi-Rec network for sequential recommendation in this paper.
To see more, please visit:
https://github.com/microsoft/recommenders/tree/master/reco_utils/recommender/deeprec/models/sequential .
Data Preparation
sh data_preparing.sh
In data/, it will generate these files:
- reviews_information
- meta_information
- train_data
- test_data
- user_vocab.pkl
- item_vocab.pkl
- category_vocab.pkl
Model Implementation
The model is implemented in model.py.
Training model:
python sli_rec/train.py
After training, run the following code to evaluate the model:
python sli_rec/test.py
The model below had been supported:
Baselines:
- ASVD
- DIN
- LSTM
- LSTMPP
- NARM
- CARNN
- Time1LSTM
- Time2LSTM
- Time3LSTM
- DIEN
Our models:
- A2SVD
- T_SeqRec
- TC_SeqRec_I
- TC_SeqRec_G
- TC_SeqRec
- SLi_Rec_Fixed
- SLi_Rec_Adaptive
Dependencies (other versions may also work):
- python==2.7
- tensorflow==1.4.1
- keras==2.1.5
- numpy==1.15.4