README

PHP port of the LightFM Python library using FFI bindings to C implementation for hybrid recommender systems.

Installation

Install via Composer:

composer require lightfm/lightfm-php

Requirements

• PHP 8.0 or higher
• FFI extension enabled
• The bundled C library (liblightfm.so)

Usage

<?php

require_once 'vendor/autoload.php';

// Create a LightFM model
$model = new LightFM([
    'no_components' => 30,
    'loss' => 'warp',
    'learning_rate' => 0.05
]);

// Fit the model with interaction data
$interactions = new CSRMatrix($data, $rows, $cols);
$model->fit($interactions);

// Get recommendations
$predictions = $model->predict($userIds, $itemIds);

Running the Examples

The repository includes a comprehensive demo script that showcases all major features of the library.

Prerequisites

1. Install dependencies:

composer install

1. Ensure the C library is compiled and available:

# The liblightfm.so file should be in the project root
ls -la liblightfm.so

Run the Demo

Execute the demo script from the project root:

php test/main.php

The demo will demonstrate:

• Loading synthetic MovieLens-like data
• Training models with WARP loss function
• Making predictions and generating recommendations
• Evaluating model performance with precision metrics
• Using user and item features
• Working with custom interaction data

Expected Output

The demo will display:

• Dataset statistics (users, items, interactions)
• Training progress and timing
• Model evaluation metrics (precision@5)
• Top recommendations for sample users
• Performance comparison with and without features
• Custom dataset predictions

Note: The current C implementation is simplified, so prediction scores may appear as zeros. This is expected behavior for the initial implementation.

Features

• Hybrid Recommender Systems: Supports both collaborative filtering and content-based filtering
• Multiple Loss Functions: WARP, BPR, and logistic loss functions
• Fast C Implementation: Uses FFI bindings to a compiled C library for performance
• CSR Matrix Support: Efficient sparse matrix operations
• Evaluation Metrics: Built-in precision, recall, and AUC metrics

Classes

• LightFM: Main model class for training and prediction
• CSRMatrix: Sparse matrix implementation for efficient data handling
• Evaluation: Evaluation metrics for model performance
• MovieLensDataset: Dataset loader for MovieLens data
• LightFMFFI: FFI bindings to the C library

License

Apache-2.0