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I’m building a restaurant recommendation system in Python using user preferences (cuisine, price range) and cosine similarity. I’m encountering a ValueError: User preference vector and restaurant feature vectors must have the same number of dimensions. error under two scenarios:

Scenario 1:

I’m using one-hot encoding for cuisine types in the restaurant data.
The get_user_preferences function considers both cuisine and price range (if provided) when building the user preference vector.
The error occurs when the user selects “all” cuisines (one-hot encoded vector with all features as 1).
Scenario 2:

The error occurs even when the user selects a specific cuisine (one-hot encoded vector with a single feature as 1).
I suspect there might be a mismatch in how cuisines are represented in the user preference vector and the restaurant data (e.g., single cuisine vs. list of cuisines).

2. Define User Preference Function

def get_user_preferences(cuisine=None, price_range=None):

Initialize an empty preference vector

user_pref = [0] * len(encoder.get_feature_names_out())

Set cuisine preference (one-hot encoding)

if cuisine:
cuisine_index = encoder.get_feature_names_out().tolist().index(cuisine)
user_pref[cuisine_index] = 1

Set price range

if price_range:
user_pref.append(price_range) # Assuming price range is a numerical value

return user_pref

def get_user_preferences(cuisine=None, price_range=None):
ist: User preference vector with one-hot encoded cuisine and price range.
“””
# Initialize an empty preference vector with equal preference for all cuisines
num_cuisines = len(encoder.get_feature_names_out())
user_pref = [1 / num_cuisines] * num_cuisines

# Set cuisine preference (one-hot encoding)
if cuisine:
    cuisine_index = encoder.get_feature_names_out().tolist().index(cuisine)
    user_pref[cuisine_index] = 1

# Set price range
if price_range:
    user_pref.append(price_range)  # Assuming price range is a numerical value

return user_pref

#3

def cosine_similarity(user_pref, restaurant_vec):
# Extract cuisine vectors from user preference and restaurant vectors
user_pref_cuisine = user_pref[:-1] if len(user_pref) > 1 else user_pref # Exclude price range if available
restaurant_vec_cuisine = restaurant_vec[:-1] if len(restaurant_vec) > 1 else restaurant_vec

Check that the user_pref_cuisine and restaurant_vec_cuisine vectors have the same length

if len(user_pref_cuisine) != len(restaurant_vec_cuisine):
    raise ValueError("User preference cuisine vector and restaurant cuisine vector must have the same number of dimensions.")

# Calculate cosine similarity
return np.dot(user_pref_cuisine, restaurant_vec_cuisine) / (np.linalg.norm(user_pref_cuisine) * np.linalg.norm(restaurant_vec_cuisine))

def get_user_preferences_from_user():
“””
This function prompts the user for their preferred cuisine and price range.

Returns:
    list: User preference vector with one-hot encoded cuisine and price range.
"""
# Get user input for cuisine
available_cuisines = list(df['Cuisines'].unique())  # Assuming unique cuisines are in a column
while True:
    cuisine = input("Enter your preferred cuisine (or 'all' for any): ").lower().strip()
    if cuisine in available_cuisines or cuisine == 'all':
        break
    else:
        print(f"Invalid cuisine. Available options are: {', '.join(available_cuisines)}")

# Get user input for price range
price_ranges = {
    "1": "Cheap",
    "2": "Moderate",
    "3": "Fine Dining"
}
while True:
    price_range = input("Enter your preferred price range (1 - Cheap, 2 - Moderate, 3 - Fine Dining): ")
    if price_range in price_ranges:
        break
    else:
        print(f"Invalid price range. Please enter 1, 2, or 3.")

# Encode cuisine (if not 'all')
if cuisine != 'all':
    cuisine_index = encoder.get_feature_names_out().tolist().index(cuisine)
    user_pref = [0] * len(encoder.get_feature_names_out())
    user_pref[cuisine_index] = 1
else:
    user_pref = [1] * len(encoder.get_feature_names_out())  # One-hot encode for all cuisines

# Add price range if specified
if price_range:
    user_pref.append(int(price_range))

# Ensure that the user preference vector has the same number of features as the restaurant feature vectors
if len(user_pref) != len(df.drop(columns=['Restaurant Name']).values[0]):
    raise ValueError("User preference vector and restaurant feature vectors must have the same number of dimensions.")

return user_pref

4. Recommend Restaurants

def recommend_restaurants(user_pref, df=df, top_n=5):
“””
This function recommends restaurants based on user preferences and similarity scores.
Args:
user_pref (list): User preference vector.
df (pandas.DataFrame, optional): Preprocessed restaurant data. Defaults to df.
top_n (int, optional): Number of top recommendations to return. Defaults to 5.
Returns:
pandas.DataFrame: Top N recommended restaurants with details.
“””
# Construct restaurant feature vectors including encoded cuisines
restaurant_feature_vectors = df.drop(columns=[‘Restaurant Name’]).values

# Check if user preference vector and restaurant feature vectors have the same number of dimensions
if len(user_pref) != len(restaurant_feature_vectors[0]):
    raise ValueError("User preference vector and restaurant feature vectors must have the same number of dimensions.")

# Calculate similarity scores for each restaurant
similarities = np.array([cosine_similarity(user_pref, vec) for vec in restaurant_feature_vectors])

# Sort restaurants by similarity score (descending)
df_sorted = df.assign(similarity=similarities).sort_values(by='similarity', ascending=False)

# Return top N recommendations
return df_sorted.head(top_n).drop('similarity', axis=1)

Assuming you have a function to get user input for cuisine and price range

user_pref = get_user_preferences_from_user() # Replace with your function

Call the recommend_restaurants function with your data and user preferences

recommendations = recommend_restaurants(user_pref, df=df) # Use your data in df

print(“Top Restaurant Recommendations:”)
print(recommendations)

Enter your preferred cuisine (or ‘all’ for any): all
Enter your preferred price range (1 – Cheap, 2 – Moderate, 3 – Fine Dining): 4
Invalid price range. Please enter 1, 2, or 3.
Enter your preferred price range (1 – Cheap, 2 – Moderate, 3 – Fine Dining): 1

ValueError Traceback (most recent call last)
in <cell line: 2>()
1 # Assuming you have a function to get user input for cuisine and price range
—-> 2 user_pref = get_user_preferences_from_user() # Replace with your function
3
4 # Call the recommend_restaurants function with your data and user preferences
5 recommendations = recommend_restaurants(user_pref, df=df) # Use your data in df

in get_user_preferences_from_user()
42 # Ensure that the user preference vector has the same number of features as the restaurant feature vectors
43 if len(user_pref) != len(df.drop(columns=[‘Restaurant Name’]).values[0]):
—> 44 raise ValueError(“User preference vector and restaurant feature vectors must have the same number of dimensions.”)
45
46 return user_pref

ValueError: User preference vector and restaurant feature vectors must have the same number of dimensions.

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