
# 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

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- 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.

<!-- ## Out-of-Scope Use -->

<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->

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# 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. 

<!-- ### Speeds, Sizes, Times -->

<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->

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# Evaluation

<!-- This section describes the evaluation protocols and provides the results. -->

<!-- ## Testing Data, Factors & Metrics -->

<!-- ### Testing Data -->

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### Factors

We've removed some of the wrong rows in the dataset.

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### Metrics

percision@k and recall@k

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<!-- ## Results -->

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### Summary

<!-- # Environmental Impact

<!-- 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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# Technical Specifications

## Model Architecture and Objective

Content-based filtering. 

<!-- ## Compute Infrastructure -->

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### Hardware

Works fine on google colab

### Software

python, sklearn, numpy, pandas

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{"license": "mit", "tags": ["code"]}
code arxiv:1910.09700 license:mit region:us
TAGS
#code #arxiv-1910.09700 #license-mit #region-us 


# 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




# 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
TAGS
#code #arxiv-1910.09700 #license-mit #region-us 
 # 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
TAGS
#code #arxiv-1910.09700 #license-mit #region-us 
# 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