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This is the repository of our article published on MDPI Entropy "Feature Selection for Recommender Systems with Quantum Computing".

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# Collaborative-driven Quantum Feature Selection

This repository was developed by Riccardo Nembrini, PhD student at Politecnico di Milano.
See the websites of our [quantum computing group](https://quantum.polimi.it/) and of our
[recommender systems group](http://recsys.deib.polimi.it/) for more information on our teams and works.
This repository contains the source code for the article "**Feature Selection for Recommender Systems with Quantum
Computing**".

Here we explain how to install dependencies, setup the connection to D-Wave Leap quantum cloud services and how to run
experiments included in this repository.

## Installation

> NOTE: This repository requires Python 3.7

It is suggested to install all the required packages into a new Python environment. So, after repository checkout, enter
the repository folder and run the following commands to create a new environment:

If you're using `virtualenv`:

```bash
virtualenv -p python3 cqfs
source cqfs/bin/activate
```

If you're using `conda`:

```bash
conda create -n cqfs python=3.7 anaconda
conda activate cqfs
```

>Remember to add this project in the PYTHONPATH environmental variable if you plan to run the experiments 
on the terminal:
>```bash
>export PYTHONPATH=$PYTHONPATH:/path/to/project/folder
>```

Then, make sure you correctly activated the environment and install all the required packages through `pip`:

```bash
pip install -r requirements.txt
```

After installing the dependencies, it is suggested to compile Cython code in the repository.

In order to compile you must first have installed: `gcc` and `python3 dev`. Under Linux those can be installed with the
following commands:

```bash
sudo apt install gcc 
sudo apt-get install python3-dev
```

If you are using Windows as operating system, the installation procedure is a bit more complex. You may refer
to [THIS](https://github.com/cython/cython/wiki/InstallingOnWindows) guide.

Now you can compile all Cython algorithms by running the following command. The script will compile within the current
active environment. The code has been developed for Linux and Windows platforms. During the compilation you may see some
warnings.

```bash
python run_compile_all_cython.py
```

## D-Wave Setup

In order to make use of D-Wave cloud services you must first sign-up to [D-Wave Leap](https://cloud.dwavesys.com/leap/)
and get your API token.

Then, you need to run the following command in the newly created Python environment:

```bash
dwave setup
```

This is a guided setup for D-Wave Ocean SDK. When asked to select non-open-source packages to install you should
answer `y` and install at least _D-Wave Drivers_ (the D-Wave Problem Inspector package is not required, but could be
useful to analyse problem solutions, if solving problems with the QPU only).

Then, continue the configuration by setting custom properties (or keeping the default ones, as we suggest), apart from
the `Authentication token` field, where you should paste your API token obtained on the D-Wave Leap dashboard.

You should now be able to connect to D-Wave cloud services. In order to verify the connection, you can use the following
command, which will send a test problem to D-Wave's QPU:

```bash
dwave ping
```

## Running CQFS Experiments

First of all, you need to prepare the original files for the datasets.

For The Movies Dataset you need to download
[The Movies Dataset from Kaggle](https://www.kaggle.com/rounakbanik/the-movies-dataset) and place the compressed files
in the directory `recsys/Data_manager_offline_datasets/TheMoviesDataset/`, making sure the file is called
`the-movies-dataset.zip`.

For CiteULike_a you need to download
[the following .zip file](https://polimi365-my.sharepoint.com/:u:/g/personal/10322330_polimi_it/EcjHpkI8TQdHnFVwVMkNGN4BmNkurMWw79sU8kpt4wk8eA?e=QYhdbz)
and place it in the directory `recsys/Data_manager_offline_datasets/CiteULike/`, making sure the file is called
`CiteULike_a_t.zip`.

We cannot provide data for Xing Challenge 2017, but if you have the dataset available, place the compressed file
containing the dataset's original files in the directory `recsys/Data_manager_offline_datasets/XingChallenge2017/`,
making sure the file is called `xing_challenge_data_2017.zip`.

After preparing the datasets, you should run the following command under the `data` directory:

```bash
python split_NameOfTheDataset.py
```

This python script will generate the data splits used in the experiments. Moreover, it will preprocess the dataset and
check for any error in the preprocessing phase. The resulting splits are saved in the
`recsys/Data_manager_split_datasets` directory.

After splitting the dataset, you can actually run the experiments. All the experiment scripts are in the `experiments`
directory, so enter this folder first.
Each dataset has separated experiment scripts that you can find in the corresponding directories.
From now on, we will assume that you are running the following commands in the dataset-specific folders, thus running
the scripts contained there.

### Collaborative models

First of all, we need to optimize the chosen collaborative models to use with CQFS. To do so, run the following command:

```bash
python CollaborativeFiltering.py
```

The resulting models will be saved into the `results` directory.

### CQFS

Then, you can run the CQFS procedure. We divided the procedure into a _selection_ phase and a _recommendation_ phase. To
perform the selection through CQFS run the following command:

```bash
python CQFS.py
```

This script will solve the CQFS problem on the corresponding dataset and save all the selected features in appropriate
subdirectories under the `results` directory.

After solving the feature selection problem, you should run the following command:

```bash
python CQFSTrainer.py
```

This script will optimize an ItemKNN content-based recommender system for each selection corresponding to the given
hyperparameters (and previously obtained through CQFS), using only the selected features. Again, all the results are
saved in the corresponding subdirectories under the `results` directory.

> NOTE: Each selection with D-Wave Leap hybrid service on these problems is performed in around 8 seconds for The Movies
> Dataset and around 30 for CiteULike_a.
> Therefore, running the script as it is would result in consuming all the free time given with the developer plan on
> D-Wave Leap and may result in errors or invalid selections when there's no free time remaining.
>
> We suggest to reduce the number of hyperparameters passed when running the experiments or, even better, chose a
> collaborative model and perform all the experiments on it.
>
> This is not the case when running experiments with Simulated Annealing, since it is executed locally.
> 
> For Xing Challenge 2017 experiments run directly on the D-Wave QPU.
> Leaving all the hyperparameters unchanged, all the experiments should not exceed the free time of the developer plan.
> Pay attention when increasing the number of reads from the sampler or the annealing time.

### Baselines

In order to obtain the baseline evaluations you can run the corresponding scripts with the following commands:

```bash
# ItemKNN content-based with all the features
python baseline_CBF.py

# ItemKNN content-based with features selected through TF-IDF
python baseline_TFIDF.py

# CFeCBF feature weighting baseline
python baseline_CFW.py
```

## Acknowledgements
Software produced by Riccardo Nembrini.
Recommender systems library by Maurizio Ferrari Dacrema.

Article authors: Riccardo Nembrini, Maurizio Ferrari Dacrema, Paolo Cremonesi

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