Building an AI-Powered Restaurant Recommendation System: A Complete Implementation from Data to Intelligent Recommendations

This article introduces an AI restaurant recommendation system inspired by Zomato, demonstrating how to combine structured data filtering with large language models (LLMs) to achieve intelligent recommendations. The project uses a modular architecture, including a data layer, preference validation, integration layer, and a recommendation engine based on the Groq API, supporting multi-dimensional user preference filtering and AI-driven personalized recommendations.

• Original Author/Maintainer: Ankur-Abhijeet
• Source Platform: GitHub
• Original Title: Zomato-milestone-1: AI-Powered Restaurant Recommendation System
• Original Link: https://github.com/Ankur-Abhijeet/Zomato-milestone-1
• Publication Date: May 28, 2026

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In today's digital age, restaurant recommendation systems have become an indispensable part of people's daily lives. From Meituan to Yelp, Zomato to OpenTable, these platforms help users quickly find options that match their taste and budget among vast amounts of restaurant information. However, traditional rule-based recommendation systems often struggle to capture users' complex preferences and contextual needs.

This project is based on this background and aims to build an AI-driven restaurant recommendation service. It is not just a simple filtering tool; instead, it combines structured data queries with the intelligent reasoning capabilities of large language models (LLMs) to provide users with a truly personalized recommendation experience. The core idea of the project is: first narrow down the candidate range through hard rules, then let AI perform intelligent sorting and explanation based on user preferences.

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The entire recommendation system uses a modular architecture implemented in phases, built step by step from the data layer to the application layer:

The system's data foundation comes from Hugging Face's public dataset; approximately 575MB of restaurant data is cached in Parquet format to improve query efficiency. The data preprocessing phase completed the following key tasks:

• Geographic location standardization: Unify city aliases (e.g., map Bengaluru to Bangalore)
• Budget classification: Automatically divide into three tiers based on the cost for two people: low (≤₹500), medium (≤₹1500), high (>₹1500)
• Rating parsing: Extract numerical values from original rating strings (e.g., "4.1/5"), exclude NEW or unrated restaurants
• Cuisine splitting: Convert comma-separated cuisine lists into queryable formats

This preprocessing ensures the efficiency and accuracy of subsequent filtering and recommendations.

The user preference module is designed with a complete validation and serialization mechanism. The core supported preference fields include:

Field	Required	Default Value	Description
location	Yes	—	City or region; blank characters are not allowed
budget	No	medium	Three tiers: low/medium/high
cuisines	No	any	List of cuisines; empty means no restriction
min_rating	No	3.0	Range: 0-5
additional	No	—	Soft preference description; max 500 characters

Notably, the design of the additional field allows users to describe additional needs in natural language (e.g., "family-friendly" or "romantic atmosphere"). These soft preferences are not used for hard filtering but are passed to the LLM as contextual references for recommendations.

The integration layer is the key bridge connecting data filtering and AI recommendations. Its processing flow follows a strict order:

1. Geographic location filtering: Precisely match the city specified by the user
2. Rating filtering: Filter restaurants that meet the minimum rating requirement
3. Budget filtering: Match the user's budget tier
4. Cuisine filtering: Check if the restaurant includes the user's preferred cuisines
5. Candidate upper limit: By default, keep a maximum of 30 restaurants (configurable up to 50)
6. Prompt construction: Format the filtered results into a structured prompt understandable by the LLM

When the filtered results are empty, the system intelligently returns skip_llm=true along with relaxation suggestions to avoid sending invalid requests to the LLM.

The recommendation engine is implemented based on the Groq API, using the llama-3.3-70b-versatile model by default. Its core features include:

• JSON mode output: Ensure structured recommendation results are returned, including ranking, restaurant name, and recommendation reasons
• Anti-hallucination validation: The system verifies whether the restaurant IDs returned by the LLM actually exist in the candidate list to prevent the model from "making up" recommendations
• Graceful degradation: When the API times out, triggers rate limits, or parsing fails, it automatically falls back to a rating-based sorting template
• Quota protection: Explicitly fails when encountering quota errors, without generating false AI text

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