Problems

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Top-K Recommendations

Recommender Systems

Easy

The final step in most recommender systems is selecting the top-K items to present to a user. Given predicted scores for all items and a set of items the user has already rated, the system must return the K highest-scoring items that the user has not yet seen.

Given a list of predicted scores (one per item), a collection of already-rated item indices, and a count K, return the indices of the top-K unrated items sorted by descending score.

Algorithm

Filter out items that appear in the rated set
Sort the remaining items by their predicted score in descending order
Return the indices of the top K items

Return at most k unrated indices ordered by descending score; break ties by smaller index.

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Examples

Input: scores = [3.5, 1.2, 4.8, 2.1, 5.0], rated_indices = [0, 2], k = 2

Output: [4, 3]

Explanation: After removing items 0 and 2, items 4 and 3 have the two highest scores.

Input: scores = [1.0, 3.0, 2.0], rated_indices = [], k = 2

Output: [1, 2]

Hint 1

Build score-index pairs only for indices absent from rated_indices.

Hint 2

Sort by negative score and then index before taking the first k pairs.

Requirements

Exclude all items whose indices appear in rated_indices
Sort remaining items by predicted score in descending order
Return the indices of the top K items as a list
Break equal scores by choosing the smaller item index first
If fewer than K items are unrated, return all unrated item indices

Constraints

scores is a non-empty list of floats
rated_indices is a list or set of valid indices
1 <= k <= len(scores)
Return a list of integers (item indices)
Time limit: 300 ms

Try Similar Problems

Hit Rate At K Precision Recall At K Ndcg Popularity Ranking Item Cf Predict

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Top-K Recommendations

Recommender Systems

Easy

Given a list of predicted scores (one per item), a collection of already-rated item indices, and a count K, return the indices of the top-K unrated items sorted by descending score.

Algorithm

Filter out items that appear in the rated set
Sort the remaining items by their predicted score in descending order
Return the indices of the top K items

Return at most k unrated indices ordered by descending score; break ties by smaller index.

Loading visualization...

Examples

Input: scores = [3.5, 1.2, 4.8, 2.1, 5.0], rated_indices = [0, 2], k = 2

Output: [4, 3]

Explanation: After removing items 0 and 2, items 4 and 3 have the two highest scores.

Input: scores = [1.0, 3.0, 2.0], rated_indices = [], k = 2

Output: [1, 2]

Hint 1

Build score-index pairs only for indices absent from rated_indices.

Hint 2

Sort by negative score and then index before taking the first k pairs.

Requirements

Exclude all items whose indices appear in rated_indices
Sort remaining items by predicted score in descending order
Return the indices of the top K items as a list
Break equal scores by choosing the smaller item index first
If fewer than K items are unrated, return all unrated item indices

Constraints

scores is a non-empty list of floats
rated_indices is a list or set of valid indices
1 <= k <= len(scores)
Return a list of integers (item indices)
Time limit: 300 ms

Try Similar Problems

Hit Rate At K Precision Recall At K Ndcg Popularity Ranking Item Cf Predict

Case 1

Case 2

scores =

Accepts: array

rated_indices =

Accepts: array

k =

Accepts: number

Given a list of predicted scores (one per item), a collection of already-rated item indices, and a count K, return the indices of the top-K unrated items sorted by descending score.

Algorithm

Filter out items that appear in the rated set
Sort the remaining items by their predicted score in descending order
Return the indices of the top K items

Return at most k unrated indices ordered by descending score; break ties by smaller index.