Rank Unseen Items by Embedding Similarity
Yahoo● EasyFULLTIMEPHONE SCREENProblem statement
You are given one user embedding and n item embeddings of the same dimension. The similarity of an item is the dot product of its embedding with the user embedding.
Exclude every item index in seenItemIndexes. Return all remaining item indices sorted by descending similarity. Break an exact score tie by the smaller item index.
Function
rankUnseenItems(userEmbedding: double[], itemEmbeddings: double[][], seenItemIndexes: int[]) → int[]Examples
Example 1
userEmbedding = [1.0,2.0]itemEmbeddings = [[1.0,0.0],[0.0,2.0],[2.0,1.0],[-1.0,0.0]]seenItemIndexes = [1]return = [2,0,3]After excluding item 1, the dot products are 4, 1, and -1 for indices 2, 0, and 3.
Example 2
userEmbedding = [1.0,1.0]itemEmbeddings = [[2.0,0.0],[0.0,2.0],[1.0,1.0]]seenItemIndexes = []return = [0,1,2]All three scores are 2, so indices determine the order.
Constraints
1 <= itemEmbeddings.length <= 100000.1 <= userEmbedding.length <= 200, and every item embedding has that length.- All coordinates are finite doubles with absolute value at most
10^6. - Seen indices are distinct and valid.