Personalized Recommendation System with Laravel & LLMs

A Personalized Recommendation System using Laravel & Large Language Models (LLMs) can be implemented to deliver user-specific content, product recommendations, or services based on preferences, interactions, and behaviors.

Overview of the System

A Personalized Recommendation System in Laravel utilizes machine learning models, particularly LLMs, to analyze user interactions and provide tailored recommendations. The system involves:

1. Data Collection – Gathering user interactions, purchases, likes, and behaviors.
2. Preprocessing & Feature Engineering – Structuring data for LLM-based predictions.
3. Recommendation Algorithm – Using LLMs & Collaborative Filtering to generate recommendations.
4. API Integration – Laravel serves as the backend, integrating LLM APIs for AI-driven recommendations.
5. Frontend Display – Laravel Blade or Vue.js renders personalized content dynamically.

Tech Stack

• Backend: Laravel (PHP 8+, Laravel 10)
• Database: MySQL/PostgreSQL
• LLM Integration: OpenAI API, Hugging Face Models, Local LLMs (Llama, Mistral, etc.)
• Vector Search: Pinecone, Weaviate, or FAISS for semantic search
• Frontend: Vue.js/React with Laravel Blade

Implementation Steps

1. Data Collection & Storage Store user preferences, clicks, and history in a Laravel database:

Migration for user_preferences table:

Schema::create('user_preferences', function (Blueprint $table) {
    $table->id();
    $table->foreignId('user_id')->constrained()->onDelete('cascade');
    $table->json('preferences'); // Store categories, interests, etc.
    $table->timestamps();
});

Model for User Preferences:

class UserPreference extends Model
{
    protected $fillable = ['user_id', 'preferences'];
    protected $casts = ['preferences' => 'array'];

    public function user()
    {
        return $this->belongsTo(User::class);
    }
}

2. Integrating LLM APIs for Recommendation
To generate recommendations, you can integrate OpenAI GPT-4, Mistral, or a custom fine-tuned LLM.

Laravel Service to Call OpenAI API:

use OpenAI;

class RecommendationService
{
    public function getRecommendations($userPreferences)
    {
        $client = OpenAI::client(env('OPENAI_API_KEY'));

        $response = $client->completions()->create([
            'model' => 'gpt-4-turbo',
            'prompt' => "Based on the user's preferences: " . json_encode($userPreferences) . 
                        ", recommend 5 products/services that they might like.",
            'max_tokens' => 200,
        ]);

        return json_decode($response['choices'][0]['text'], true);
    }
}

3. Implementing a Recommendation API in Laravel
Expose recommendations via a Laravel API.

Controller to Fetch Personalized Recommendations:

use App\Models\UserPreference;
use App\Services\RecommendationService;

class RecommendationController extends Controller
{
    protected $recommendationService;

    public function __construct(RecommendationService $recommendationService)
    {
        $this->recommendationService = $recommendationService;
    }

    public function getRecommendations($userId)
    {
        $preferences = UserPreference::where('user_id', $userId)->first();

        if (!$preferences) {
            return response()->json(['message' => 'No preferences found'], 404);
        }

        $recommendations = $this->recommendationService->getRecommendations($preferences->preferences);

        return response()->json($recommendations);
    }
}

API Route

Route::get('/recommendations/{userId}', [RecommendationController::class, 'getRecommendations']);

4. Using Vector Databases for Semantic Search
To enhance recommendations, you can use FAISS (Facebook AI Similarity Search) or Pinecone to store and retrieve embeddings.

Generating Embeddings for Products
Use OpenAI’s text-embedding-ada-002 model to convert product descriptions into vectors.

use OpenAI;

class EmbeddingService
{
    public function generateEmbedding($text)
    {
        $client = OpenAI::client(env('OPENAI_API_KEY'));

        $response = $client->embeddings()->create([
            'model' => 'text-embedding-ada-002',
            'input' => $text,
        ]);

        return $response['data'][0]['embedding'];
    }
}

Store these embeddings in a database or Pinecone to match user preferences efficiently.

5. Frontend Integration in Laravel
Display recommendations dynamically using Laravel Blade or Vue.js.

Blade View Example

@foreach ($recommendations as $recommendation)
    <div class="recommendation-item">
        <h3>{{ $recommendation['title'] }}</h3>
        <p>{{ $recommendation['description'] }}</p>
    </div>
@endforeach

Enhancements & Next Steps

• Fine-tune LLMs – Train custom models on user behavior data.
• Hybrid Recommendation Model – Combine LLMs with traditional collaborative filtering.
• Real-time Recommendations – Implement WebSockets for live user feedback.
• A/B Testing – Compare LLM-based recommendations vs. traditional approaches.