https://github.com/mochatek/cbrecommender
Python library for implementing Content-Based Recommendation Engines with ease!
https://github.com/mochatek/cbrecommender
content-based-recommendation python3 recommendation-engine
Last synced: 6 months ago
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Python library for implementing Content-Based Recommendation Engines with ease!
- Host: GitHub
- URL: https://github.com/mochatek/cbrecommender
- Owner: mochatek
- License: mit
- Created: 2020-01-21T15:17:13.000Z (over 6 years ago)
- Default Branch: master
- Last Pushed: 2022-07-30T04:35:42.000Z (almost 4 years ago)
- Last Synced: 2025-12-02T23:24:06.108Z (7 months ago)
- Topics: content-based-recommendation, python3, recommendation-engine
- Language: Python
- Homepage:
- Size: 21.5 KB
- Stars: 4
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE.txt
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README
# cbrecommender
[](https://pepy.tech/project/cbrecommender)
[](https://pepy.tech/project/cbrecommender)
[](https://pepy.tech/project/cbrecommender)
cbrecommender is a Python library for implementing Content-Based Recommendation Engines with ease!
**A Content-Based Recommender** is a form of **Personalized recommendation System** that maintains a user profile and tries to match the items with the taste profile of a user before presenting them as a recommendation to the user.
`The key ideas are:`
> - Model items according to relevant attributes derived from the content.
> - Develop a user profile either from their implicit actions (clicks, time spend on a video etc.), explicit actions(purchase, rating etc.) or by combining both.
> - Use these profiles to provide recommendations.
## Installation
Install from pypi with `pip` :
```shell
pip install cbrecommender
```
## Usage
**1. Importing and initializing :**
```python
from cbrecommender import CBRecommender
recommender = CBRecommender()
```
**2. Creating _Item Profiles_ :**
In Content-Based Recommender, we must build a profile for each item, which will represent the important characteristics of that item.
```python
item_profiles = recommender.create_item_profile(features)
```
- `features: DataFrame` must be relevant attributes of the item that signifies the user's preferences. For example: movie genres, news topics, post tags etc.
- `create_item_profile() -> DataFrame` will return the item_profiles created from the supplied features.
**3. Creating _User Profile_ :**
```python
user_profile = recommender.fit(train_item_profiles, scores)
```
- `fit() -> DataFrame` is where we extract user preferences from the item-profiles and associated scores, and then construct the user-profile.
- `train_item_profiles: DataFrame` must be a subset of the item-profiles created at _step 2_. For example, it can be the item-profiles of the movies already watched by the user (watch history).
- `scores: List[float]` must be a list of some measure corresponding to each item in _train_item_profiles_, denoting how much the user liked that item. For example: Rating for a watched movie, song etc.
**4. Get recommendations based on _User Profile_ :**
```python
recommendations = recommender.recommend(test_items, test_item_profiles, min_score, limit)
```
- `test_items: DataFrame` must be those items that the user have not used for training and from which we need recommendations. For example: Unwatched movies.
- `test_item_profiles: DataFrame` must be the item-profiles of _test_items_.
- `min_score: float` must be a numerical value (1-10) that specifies the minimum score for recommending items. Default is 7.5.
- `limit: int` must be an integer that denotes the number of items to recommended.
## Example
```python
from cbrecommender import CBRecommender
from pandas import DataFrame
```
```python
data = DataFrame(
{'movie':['Endgame','Avatar','Titanic','Infinity War','Jurassic World','Black Panther',
'Harry Potter-II','The Last Jedi'],
'genre':['Action,Adventure,Drama','Action,Adventure,Fantasy','Drama,Romance',
'Action,Adventure,Sci-Fi','Action,Adventure,Sci-Fi','Action,Adventure,Sci-Fi',
'Adventure,Drama,Fantasy','Action,Adventure,Fantasy']
})
print(data)
```
| movie | genre |
| --------------- | ------------------------ |
| Endgame | Action,Adventure,Drama |
| Avatar | Action,Adventure,Fantasy |
| Titanic | Drama,Romance |
| Infinity War | Action,Adventure,Sci-Fi |
| Jurassic World | Action,Adventure,Sci-Fi |
| Black Panther | Action,Adventure,Sci-Fi |
| Harry Potter-II | Adventure,Drama,Fantasy |
| The Last Jedi | Action,Adventure,Fantasy |
```python
recommender = CBRecommender()
# We are considering genre alone as the feature. You can include other features as well.
movie_profiles = recommender.create_item_profile(data[['genre']])
print(movie_profiles)
```
| action | adventure | drama | fantasy | romance | sci-fi |
| ------ | --------- | ----- | ------- | ------- | ------ |
| 1 | 1 | 1 | 0 | 0 | 0 |
| 1 | 1 | 0 | 1 | 0 | 0 |
| 0 | 0 | 1 | 0 | 1 | 0 |
| 1 | 1 | 0 | 0 | 0 | 1 |
| 1 | 1 | 0 | 0 | 0 | 1 |
| 1 | 1 | 0 | 0 | 0 | 1 |
| 0 | 1 | 1 | 1 | 0 | 0 |
| 1 | 1 | 0 | 1 | 0 | 0 |
```python
# Consider we had watched the first 4 movies. So we use it as training data to extract preferences.
# We use the user rating for the watched movies as the preference score.
watched_movie_profiles = movie_profiles.iloc[:4, :]
watched_movie_ratings = [8.5,7.8,7.8,8.5]
user_profile = recommender.fit(watched_movie_profiles, watched_movie_ratings)
print(recommender.user_profile)
```
| action | adventure | drama | fantasy | romance | sci-fi |
| ------ | --------- | ------ | ------- | ------- | ------ |
| 0.2755 | 0.2755 | 0.1811 | 0.0866 | 0.0866 | 0.0944 |
```python
# We use the remaining 4 unwatched movies as test data to get recommendations from.
unwatched_movies = data[['movie']].iloc[4:,:]
unwatched_movie_profiles = movie_profiles.iloc[4:,:]
# Recommend top 3 movies with minimum expected rating of 5.0
recommendations = recommender.recommend(unwatched_movies, unwatched_movie_profiles, 5.0, 3)
print(recommendations)
```
| item | expected score |
| -------------- | -------------- |
| Jurassic World | 6.45 |
| Black Panther | 6.45 |
| The Last Jedi | 6.37 |
## Contributing
Pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.
## License
[MIT License ](https://github.com/mochatek/cbrecommender/blob/master/LICENSE.txt)