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The Recommendation Engine finds real Nigerian restaurants and ranks them by how well they match your taste profile. It pulls live data from the Google Places API, filters it for cultural relevance, and returns up to five picks with a reason, a predicted rating, and a cultural note for each. You can use it through the web app or call it directly from the API.

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How it works

The engine takes signals about what you want — your city, food preference, price range, and optionally your GPS coordinates — and queries Google Places for matching restaurants. It then scores each result against your persona or cold-start signals to produce a ranked list. Each recommendation includes why it was picked and practical cultural context, like the best time to visit.
Recommendations are powered by real-time Google Places data, so results reflect currently operating restaurants and may change over time.

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Two ways to get recommendations in the app

You can reach the recommendation engine through either interface in the app:

Chat mode

Type your craving in plain language — “cheap suya near me” or “family spot in Abuja” — and the engine interprets your intent automatically.

Form mode

Fill in structured fields for city, food type, and price range. Good for precise queries when you know exactly what you want.

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Cold-start recommendations

New users without a review history can still get accurate picks. You provide a few signals — city, preferred food, and price range — and the engine uses those directly to find matches. This is called a cold-start request. Cold-start signals are passed under the cold_start_signals key in the API request body. You don’t need a user_id.

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GPS recommendations

When you grant location access in the app, your coordinates are included in the request. The engine uses them to surface restaurants within a short distance of where you are, ranked by proximity alongside cultural fit. If you deny location access, the engine falls back to city-level recommendations using the city you specify or select. You still get relevant results — just not hyper-local ones.

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Supported cities

CityCountry
LagosNigeria
AbujaNigeria
Port HarcourtNigeria
IbadanNigeria
KanoNigeria

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API usage

Send a POST request to https://naijataste-api-vcp4.onrender.com/recommend. The examples below show a cold-start request and a GPS-enabled request. 
{
  "cold_start_signals": {
    "city": "Lagos",
    "preferred_food": "local Nigerian buka amala",
    "price_range": "budget"
  }
}

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Response fields

Each object in the response array contains these fields:
FieldTypeDescription
item_namestringThe name of the restaurant as it appears in Google Places.
reasonstringA brief explanation of why this restaurant was recommended for your query.
predicted_ratingnumberThe engine’s predicted rating for this restaurant given your persona or signals, on a scale of 1 to 5.
cultural_notestringPractical local context — for example, the best time to visit or what the crowd is typically like.