Recommends similar dishes.
Parameters:
| Name |
Type |
Description |
Default |
dish_id |
int |
Dish id which koolsla recommends similar ones.
|
required |
recommendation_count |
int |
Max recommendation count given by user.
|
5 |
Returns:
| Type |
Description |
Optional[dict] |
indice, similarity value (dictionary): Indice & similarity value or None.
|
Source code in koolsla/recommender.py
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def recommend(dish_id: int, recommendation_count: int = 5) -> Optional[dict]:
"""Recommends similar dishes.
Args:
dish_id (int): Dish id which koolsla recommends similar ones.
recommendation_count (int): Max recommendation count given by user.
Returns:
indice, similarity value (dictionary): Indice & similarity value or None.
"""
is_valid = data.validate_dish_id(dish_id)
if is_valid:
# Import the data
imported_data = data.import_data()
# Get the metadatasets
dataset = data.split_data(imported_data)
# Train the recommendation engine
tfidf_matrix = tfidf.train_engine(dataset['names'])
# Generate recommendations
recomended_dishes = tfidf.find_similarities(tfidf_matrix, dish_id, recommendation_count)
# Input dish
color_print.print_green('Given Dish')
color_print.print_dish(
name=dataset['names'][dish_id],
dish_id=dish_id,
color=color_print.green)
# Display the recommended dishes
color_print.print_yellow('Top ' + str(recommendation_count) + ' Recommendations')
for i, _ in recomended_dishes:
color_print.print_green(i)
color_print.print_dish(
name=dataset['names'][i],
dish_id=i,
color=color_print.yellow)
return recomended_dishes
return None
|