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on_boarding.md

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graph LR
    Data_Module["Data Module"]
    Core_Layers_Utilities["Core Layers & Utilities"]
    Matching_Models["Matching Models"]
    Ranking_Models["Ranking Models"]
    Evaluation_Module["Evaluation Module"]
    Data_Module -- "provides preprocessed input data to" --> Matching_Models
    Data_Module -- "supplies structured input features to" --> Ranking_Models
    Data_Module -- "provides test datasets and ground truth labels for" --> Evaluation_Module
    Core_Layers_Utilities -- "offers reusable neural network layers and loss functions to" --> Matching_Models
    Core_Layers_Utilities -- "integrates core layers and loss functions as building blocks for" --> Ranking_Models
    Matching_Models -- "outputs predictions for performance assessment by" --> Evaluation_Module
    Ranking_Models -- "outputs predictions for performance assessment by" --> Evaluation_Module
    click Data_Module href "https://github.com/CodeBoarding/GeneratedOnBoardings/blob/main/RecLearn/Data_Module.md" "Details"
    click Evaluation_Module href "https://github.com/CodeBoarding/GeneratedOnBoardings/blob/main/RecLearn/Evaluation_Module.md" "Details"
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Details

The RecLearn project is structured around a clear, modular architecture designed for developing and evaluating recommender systems. The Data Module serves as the entry point for all data, handling its preparation and distribution to downstream components. Core Layers & Utilities provides fundamental building blocks, including neural network layers and loss functions, which are leveraged by both Matching Models and Ranking Models. These two model components represent distinct stages in a typical recommender system pipeline: Matching Models efficiently retrieve candidate items, while Ranking Models refine this selection by ordering them by relevance. Finally, the Evaluation Module consumes the outputs from both Matching and Ranking Models, along with ground truth data from the Data Module, to assess overall system performance. This design promotes reusability, clear separation of concerns, and facilitates independent development and testing of each architectural component.

Data Module [Expand]

Responsible for all aspects of data handling, including loading, preprocessing, transformation, and splitting datasets for training, validation, and testing. It ensures data is in the correct format for downstream model consumption.

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Core Layers & Utilities

Provides foundational, reusable neural network building blocks and common utility functions, including various loss functions essential for model training. These layers are designed to be model-agnostic.

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Matching Models

Encapsulates various recommender system models specifically designed for the "matching" task. These models aim to efficiently retrieve a set of relevant candidate items from a large corpus.

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Ranking Models

Contains implementations of recommender system models focused on the "ranking" task. These models take a smaller set of candidate items and order them by predicted relevance.

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Evaluation Module [Expand]

Handles the quantitative assessment of recommender system performance using various metrics. It consumes model predictions and ground truth data to provide insights into model effectiveness.

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