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Temporal Filtering

TemporalFilteringRecommender

TemporalFilteringRecommender is a class designed to provide recommendations based on temporal filtering techniques. This class is part of a content filtering engine that utilizes time-based data to enhance the relevance of recommendations.

The primary goal of temporal filtering is to incorporate the dimension of time into the recommendation process, allowing for more dynamic and contextually relevant suggestions. This can be particularly useful in scenarios where user preferences or item popularity change over time.

Usage

(Provide a brief example of how to use this class, if applicable)

Note

This class is currently a placeholder and does not contain any implemented methods. Future versions will include methods for fitting the model to data, making predictions, and updating recommendations based on new temporal data.

Source code in engines/contentFilterEngine/special_techniques/temporal_filtering.py 
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class TemporalFilteringRecommender:
    """
    TemporalFilteringRecommender is a class designed to provide recommendations
    based on temporal filtering techniques. This class is part of a content 
    filtering engine that utilizes time-based data to enhance the relevance of 
    recommendations.

    The primary goal of temporal filtering is to incorporate the dimension of 
    time into the recommendation process, allowing for more dynamic and 
    contextually relevant suggestions. This can be particularly useful in 
    scenarios where user preferences or item popularity change over time.

    Attributes:
        (Define any attributes here, if applicable, e.g., time_window, data_source)

    Methods:
        (List any methods here, if applicable, e.g., fit, predict, update)

    Usage:
        (Provide a brief example of how to use this class, if applicable)

    Note:
        This class is currently a placeholder and does not contain any 
        implemented methods. Future versions will include methods for fitting 
        the model to data, making predictions, and updating recommendations 
        based on new temporal data.

    """
    pass