As recommender systems today play an important role in our online experience and are involved in a wide range of decisions, multiple stakeholders demand explanations for the corresponding algorithmic predictions. These demands---together with the benefits of explanations (e.g., trust, efficiency, and sometimes even persuasion)--- have triggered significant interest from researchers in academia and industry.
Nonetheless, to the best of our knowledge, no comprehensive toolkit for explainable recommender systems is available to the community yet. Instead, researchers are frequently faced with the challenge of re-implementing prior algorithms when creating and evaluating new approaches. Aiming to address the resulting need, we introduce RecoXplainer, a software toolkit that includes several state-of-the-art explainability methods, and two evaluation metrics.
The following toolkits are necessary:
- conda
- git
Clone the repo:
git clone https://github.com/ludovikcoba/recoxplainer.git
Create environment on conda:
conda create -n recoxplainer python=3.6
RecoXplainer was developed with python 3.6. Activate the new environment:
conda activate recoxplainer
Install torch as explained in https://pytorch.org/, we are using the version without CUDA.
When torch is installed navigate to the folder where you cloned the library and run:
pip install -r requirements.txt
This command will install all the dependencies. Next, install the recoxplainer:
pip install -e .
And finally run the notebooks:
jupyter notebook
Running times for pre-processing, training, recommendation, explanation, and evaluation can be found in the `running_times.cvs' file. The reported time was calculated using a MacBook Pro 2,3 GHz Dual-Core Intel Core i5, 8 GB 2133 MHz LPDDR3.