Build a Recommendation Engine

Dappier’s AI Recommendations API allows you to build a recommendation engine that can be used to personalize user experiences. This guide will walk you through the process of building a recommendation engine using Dappier’s AI Recommendations API.

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Prerequisites

Before you begin, you’ll need to sign up for a Dappier account and create an API key.

1. Visit the Dappier Platform to sign up.

2. Create an API key under Settings > Profile > API Keys.

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Step 1: Get Data Model ID

To get started, you’ll need to pick a data model from the Dappier Marketplace. The data model ID is required to make API calls to the AI Recommendations API.

1. Browse through the data models available in the Dappier Marketplace.

2. Select the data model that best fits your use case.

3. Get the data model ID from the request endpoint. The data model ID starts with dm_.

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Step 2: Make API Call

Once you have the data model ID, you can make API calls to the AI Recommendations API to get personalized recommendations.

Here’s an example of how you can make an API call using Python:

import requests
import json

url = "https://api.dappier.com/app/datamodel/dm_01j0pb465keqmatq9k83dthx34"
payload = json.dumps({
    "query": "lifestyle new articles",
    "similarity_top_k": 3,
    "ref": "",
    "num_articles_ref": 0,
    "search_algorithm": "most_recent"
})
headers = {
    'Content-Type': 'application/json',
    'Authorization': 'Bearer <YOUR_DAPPIER_API_KEY>'
}

response = requests.post(url, headers=headers, data=payload)

print(response.text)

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Step 3: Analyze Recommendations

Once you’ve made the API call, you’ll receive a response with personalized recommendations based on the input query.

You can analyze the recommendations and use them to personalize user experiences in your application.

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Conclusion

Building a recommendation engine with Dappier’s AI Recommendations API is a powerful way to enhance user experiences and drive engagement.