This content originally appeared on DEV Community and was authored by Susheel kumar
Saksh Recommendation Engine
A powerful recommendation engine designed for e-commerce applications, leveraging the capabilities of ChatGPT.
10 Benefits of Using the saksh-recommendation-engine Package
Personalized Recommendations
Provides personalized product recommendations based on user profile data, enhancing the shopping experience.Boost Revenue
By suggesting relevant products, it can increase the likelihood of purchases, boosting overall sales.Improved Customer Satisfaction
Personalized recommendations make users feel understood and valued, leading to higher customer satisfaction.Enhanced User Engagement
By showing products that match user interests, it keeps users engaged and encourages them to spend more time on your site.Cross-Selling Opportunities
Suggests complementary products, increasing the average order value through cross-selling.Reduced Cart Abandonment
Helps reduce cart abandonment by reminding users of items they showed interest in but didn't purchase.Data-Driven Insights
Analyzes user data to provide insights into customer preferences and behavior, helping you make informed business decisions.Scalability
Designed to handle a growing number of users and products, making it suitable for businesses of all sizes.Easy Integration
Easy to integrate into existing Node.js applications, with clear documentation and examples.Leverages AI Technology
Utilizes ChatGPT and machine learning algorithms to provide accurate and relevant recommendations, staying ahead of traditional recommendation systems.
These benefits can significantly enhance the functionality and user experience of your e-commerce platform, leading to increased customer loyalty and business growth.
Installation
To install the package, simply use npm:
npm install saksh-recommendation-engine
Usage
Here’s how to use the Saksh Recommendation Engine in your application:
const { sakshGetRecommendations } = require('saksh-recommendation-engine');
const apiKey = 'your_openai_api_key';
const productCatalog = [
{ id: 1, description: "Product A description" },
{ id: 2, description: "Product B description" },
{ id: 3, description: "Product C description" },
// Add more products as needed
];
const userProfile = {
userId: "12345",
demographics: {
age: 30,
gender: "female",
location: "New York, USA",
incomeLevel: "75,000-100,000",
education: "Bachelor's Degree",
occupation: "Software Engineer"
},
behavioralData: {
browsingHistory: ["product1", "product2", "category1"],
purchaseHistory: ["product3", "product4"],
searchQueries: ["laptop", "wireless headphones"],
cartAbandonment: ["product5"]
},
psychographicData: {
interests: ["technology", "fitness", "travel"],
lifestyle: ["active", "health-conscious"],
brandAffinities: ["BrandA", "BrandB"]
},
technographicData: {
device: "mobile",
browser: "Chrome",
os: "iOS",
techProficiency: "high"
},
transactionalData: {
paymentMethods: ["credit card", "PayPal"],
orderFrequency: "monthly",
averageOrderValue: 150
},
engagementData: {
emailInteractions: {
openRate: 0.75,
clickThroughRate: 0.25
},
socialMediaActivity: {
likes: 50,
comments: 10,
shares: 5
},
customerSupportInteractions: {
tickets: 2,
satisfaction: "high"
}
}
};
sakshGetRecommendations(apiKey, userProfile, productCatalog).then(recommendations => {
console.log("Recommended Products:", recommendations);
}).catch(error => {
console.error("Error getting recommendations:", error);
});
License
This project is licensed under the MIT License. See the LICENSE file for details.
Support
For support, please contact: susheel2339@gmail.com
This content originally appeared on DEV Community and was authored by Susheel kumar
Susheel kumar | Sciencx (2024-09-10T06:39:14+00:00) Saksh Recommendation Engine. Retrieved from https://www.scien.cx/2024/09/10/saksh-recommendation-engine/
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