Rank Listings by Distance and Value | Dataford Interview Questions

Optimize a search query algorithm to rank Neighbor listings based on distance, price, and host responsiveness.

Implement rank_listings(listings, query, k). Each listing contains an ID and three numeric attributes. Normalize each attribute against the corresponding query maximum, compute a weighted score where lower distance, price, and response time are better, and return the IDs of the best k listings. Break equal scores by original input order. Use a bounded top-k approach rather than sorting every listing when possible.

The function accepts JSON-compatible lists and dictionaries and returns a list of listing ID strings.

Constraints

1 <= len(listings) <= 500
0 <= k <= len(listings)
Each listing ID is a unique string
distance_miles, price_cents, and response_minutes are nonnegative numbers
max_distance, max_price, and max_response are positive numbers
All listing attribute values are at most their corresponding query maximum
All weights are nonnegative numbers

Function Signature

def rank_listings(listings, query, k):

Your question is Rank Listings by Distance and Value. Start with the requirements in the Question tab.

Run and submit as often as you like. When you're ready, talk me through your approach or go straight to the code.

Problem

Optimize a search query algorithm to rank Neighbor listings based on distance, price, and host responsiveness.

The function accepts JSON-compatible lists and dictionaries and returns a list of listing ID strings.

Constraints

1 <= len(listings) <= 500
0 <= k <= len(listings)
Each listing ID is a unique string
distance_miles, price_cents, and response_minutes are nonnegative numbers
max_distance, max_price, and max_response are positive numbers
All listing attribute values are at most their corresponding query maximum
All weights are nonnegative numbers

Function Signature

def rank_listings(listings, query, k):

Your question is Rank Listings by Distance and Value. Start with the requirements in the Question tab.

Run and submit as often as you like. When you're ready, talk me through your approach or go straight to the code.