Your question is Find Most Similar Places. Start with the requirements on the right.
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Yelp wants to recommend places similar to a user-selected business. Given a collection of places, a target place ID, and a number k, return the IDs of the k most similar other places.
Each place is represented by a dictionary containing an id, a two-dimensional location, and a list of categories. Similarity is defined by the Jaccard index of the category sets: intersection_size / union_size. Higher Jaccard similarity ranks first. If two places have equal category similarity, the place with the smaller squared Euclidean distance from the target ranks first. If both values are equal, sort by place ID in lexicographic order.
Implement find_similar_places(places, target_id, k), where places is a list of dictionaries and each location is [x, y]. Return a list of place ID strings. Exclude the target place itself. If fewer than k other places exist, return all available places.
def find_similar_places(places, target_id, k):