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tags: |
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- code |
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license: mit |
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--- |
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# Model Card for Model ID |
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This model recommends movies to users based on the movies they have voted for. |
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# Model Details |
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The model consists of three parts |
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- Content-based filtering |
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- Collaborative filtering |
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- Ensemble model |
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## Model Description |
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Content-based filtering is used for recommending movies based on the content of their previously voted movies. e.g. genre, actors, ... |
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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. |
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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. |
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- Developed by: Aida Aminian, Mohammadreza Mohammadzadeh Asl |
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<!--- Shared by [optional]: [More Information Needed]--> |
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- Model type: content-based filtering and collaborative and an ensemble model of these two model |
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- Language(s) (NLP): not used, only TFIDF for keywords is used |
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- License: MIT License |
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## Model Sources |
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MovieLens dataset |
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# Uses |
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Building recommendation systems |
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## Direct Use |
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Movie recommendations based on content and other similar people. |
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<!-- ## Out-of-Scope Use --> |
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<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> |
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<!-- [More Information Needed] --> |
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# Bias, Risks, and Limitations |
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This ML model is based on an IMDB movie dataset. The dataset may have more focus on English movies. |
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## Recommendations |
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Add other metrics to model |
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## How to Get Started with the Model |
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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. |
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# Training Details |
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## Training Data |
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IMDB Movies |
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### Preprocessing |
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Extracting features from keywords. |
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<!-- ### Speeds, Sizes, Times --> |
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<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> |
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<!-- [More Information Needed] --> |
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# Evaluation |
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<!-- This section describes the evaluation protocols and provides the results. --> |
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<!-- ## Testing Data, Factors & Metrics --> |
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<!-- ### Testing Data --> |
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<!-- This should link to a Data Card if possible. --> |
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<!-- [More Information Needed] --> |
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### Factors |
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We've removed some of the wrong rows in the dataset. |
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<!-- [More Information Needed] --> |
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### Metrics |
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percision@k and recall@k |
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<!-- [More Information Needed] --> |
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<!-- ## Results --> |
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<!-- [More Information Needed] --> |
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### Summary |
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<!-- # Environmental Impact |
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<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> |
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<!-- Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). --> |
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<!-- - Hardware Type: [More Information Needed] --> |
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<!-- - Hours used: [More Information Needed] --> |
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<!-- - Cloud Provider: [More Information Needed] --> |
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<!-- - Compute Region: [More Information Needed] --> |
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<!-- - Carbon Emitted: [More Information Needed] --> |
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# Technical Specifications |
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## Model Architecture and Objective |
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Content-based filtering. |
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<!-- ## Compute Infrastructure --> |
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<!-- [More Information Needed] --> |
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### Hardware |
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Works fine on google colab |
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### Software |
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python, sklearn, numpy, pandas |
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<!-- # Model Card Contact --> |
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<!-- [More Information Needed] --> |
