tags:
- code
license: mit
Model Card for Model ID
This model recommends movies to users based on the movies they have voted for.
Model Details
The model consists of three parts
Content-based filtering
Collaborative filtering
Ensemble model
Model Description
Content-based filtering is used for recommending movies based on the content of their previously voted movies. e.g. genre, actors, ...
By using collaborative filtering, similar interests are found and movies that have been voted for by some users are recommended to users who have not voted for them. It doesn't depend on the content and doesn't need domain knowledge.
The ensemble model is created by combining the last two methods to give better recommendations. The algorithm finds similar people and then recommends films based on their votes, filtering them based on content preferences.
- Developed by: Aida Aminian, Mohammadreza Mohammadzadeh Asl
Model type: content-based filtering and collaborative and an ensemble model of these two model
Language(s) (NLP): not used, only TFIDF for keywords is used
License: MIT License
Model Sources
MovieLens dataset
Uses
Building recommendation systems
Direct Use
Movie recommendations based on content and other similar people.
Bias, Risks, and Limitations
This ML model is based on an IMDB movie dataset. The dataset may have more focus on English movies.
Recommendations
Add other metrics to model
How to Get Started with the Model
Install the sklearn, pandas and numpy libraries for python. Download the MovieLens dataset and put that in the 'content/IMDB' path in the project directory. Use python interpreter to run the code.
Training Details
Training Data
IMDB Movies
Preprocessing
Extracting features from keywords.
Evaluation
Factors
We've removed some of the wrong rows in the dataset.
Metrics
percision@k and recall@k
Summary
Technical Specifications
Model Architecture and Objective
Content-based filtering.
Hardware
Works fine on google colab
Software
python, sklearn, numpy, pandas