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Machine learning movie recommending system
| Date | Stars |
|---|---|
| 2026-07-31 | 534 |
| 2026-08-06 | 534 |
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<h1 align="center">
Moviebox
</h1>
<h4 align="center">
Machine learning movie recommending system
</h4>
<div align="center">
<img src="media/header.png" alt="Moviebox" width="90%">
</div>
## Contents
- [Statement from the Authors](#statement-from-the-authors)
- [Description](#description)
- [CLI](#cli)
- [Usage](#usage)
- [API](#api)
- [Development](#development)
- [Team](#team)
- [License](#license)
## Statement from the Authors
Moviebox is a software implementation of academic research nature authored by [Mario Sinani](https://github.com/mariosinani) and [Klaudio Sinani](https://github.com/klaudiosinani), published on the GitHub.com public domain on [January 23, 2018](https://github.com/klaudiosinani/moviebox/commits/master/?after=f21fcee45d565584128d10ee1f86518d9748bf98+69), and the PyPI.org public registry on [January 26, 2018](https://pypi.org/project/moviebox/#history), under the [MIT](https://github.com/klaudiosinani/moviebox/blob/master/license.md) license.
## Description
Moviebox is a content based machine learning recommending system build with the powers of [`tf-idf`](https://en.wikipedia.org/wiki/Tf%E2%80%93idf) and [`cosine similarities`](https://en.wikipedia.org/wiki/Cosine_similarity).
Initially, a natural number, that corresponds to the ID of a unique movie title, is accepted as input from the user. Through `tf-idf` the plot summaries of 5000 different movies that reside in the dataset, are analyzed and vectorized. Next, a number of movies is chosen as recommendations based on their `cosine similarity` with the vectorized input movie. Specifically, the cosine value of the angle between any two non-zero vectors, resulting from their inner product, is used as the primary measure of similarity. Thus, only movies whose story and meaning are as close as possible to the initial one, are displayed to the user as recommendations.
The [dataset](moviebox/dataset/movies.csv) in use is a random subset of the [Carnegie Mellon Movie Summary Corpus](http://www.cs.cmu.edu/~ark/movie$-data/), and it consists of `5000` movie titles along with their respective categories and plots.
## Install
```bash
pip install moviebox
```
**`Python 2.7+`** or **`Python 3.4+`** is required to install or build the code.
## CLI
```bash
$ moviebox --help
Machine learning movie recommending system
Usage
$ moviebox [<options> ...]
Options
--help, -h Display help message
--search, -s Search movie by ID
--movie, -m <int> Input movie ID [Can be any integer 0-4999]
--plot, -p Display movie plot
--interactive, -i Display process info
--list, -l List available movie titles
--recommend, -r <int> Number of recommendations [Can be any integer 1-30]
--version, -v Display installed version
Examples
$ moviebox --help
$ moviebox --search
$ moviebox --movie 2874
$ moviebox -m 2874 --recommend 3
$ moviebox -m 2874 -r 3 --plot
$ moviebox -m 2874 -r 3 -p --interactive
```
To see all movies with corresponding ID's, take a look [at this list](movie-titles.md).
## Usage
```python
from moviebox.recommender import recommender
movieID = 2874 # Movie ID of `Asterix & Obelix: God save Britannia`
recommendationsNumber = 3 # Get 3 movie recommendations
showPlots = True # Display the plot of each recommended movie
interactive = True # Display process info while running
# Generate the recommendations
recommender(
movieID=movieID,
recommendationsNumber=recommendationsNumber,
showPlots=showPlots,
interactive=interactive)
```
## API
### recommender`(movieID, recommendationsNumber, showPlots, interactive)`
**E.g.** `recommender(movieID=2874, recommendationsNumber=3, showPlots=True, interactive=True)`
#### `movieID`
- Type: `Integer`
- Default Value: `2874`
- Optional: `True`
Input movie ID. Any integer between `[0, 4999]` can be selected.
#### `recommendationsNumber`
- Type: `IntegerExcerpt of 5,005 characters
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