A-Dynamic-Meta-Learning-Model-for-Time-Sensitive-Cold-Start-Recommendations

This is a Pytorch implementation of our model:

A novel dynamic recommendation model that focuses on users who have interactions in the past but turn relatively inactive recently i.e. time-sensitive cold-start users.

Installation

1. Clone this repository.

   git clone https://github.com/ritmininglab/A-Dynamic-Meta-Learning-Model-for-Time-Sensitive-Cold-Start-Recommendations
   cd A-Dynamic-Meta-Learning-Model-for-Time-Sensitive-Cold-Start-Recommendations

2. Install the following dependencies. The code should run with Pytorch 1.3.1 and newer.

• Pytorch (1.3.x)
• python 3.5 or newer
• scikit-learn
• scipy
• numpy
• pickle

Run

1. Go to each folders of datasets to run the corresponding experiments.
2. For example cd Netflix and
3. Run python proposed_model.py for the Netflix dataset

Base

This code is based on MeLU