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sli_rec's Introduction

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.

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

sli_rec's People

Contributors

ijcaipaper1329 avatar

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