Improving Top-N Evaluation of Recommender Systems
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Recommender systems are used to provide the user with a list of recommended items to help user find new items they might prefer. One of the main task of the recommender is to provide such items that the user has not seen before. But while evaluating, if the recommender correctly predicts such items we penalize the recommender, usually because the relevance of the item for that user is unknown, and because of the unknown relevance the item being recommended was not present in the test set of the recommender. In recommender systems it is very hard to get the relevance of every item for every user. In this research we are trying to address this problem by randomly adding decoys into the recommender’s test set. We will be measuring the performance of the recommender with different decoy sizes. We find that random decoys are exaggerating the advantage of popular-item recommenders, casting doubt on their usefulness.