The long tail of recommender systems and how to leverage it

This paper splits the whole itemset into the head and the tail parts and clusters only the tail items, and shows that this reduces the recommendation error rates for the Tail items, while maintaining reasonable computational performance.

Abstract

The paper studies the Long Tail problem of recommender systems when many items in the Long Tail have only few ratings, thus making it hard to use them in recommender systems. The approach presented in the paper splits the whole itemset into the head and the tail parts and clusters only the tail items. Then recommendations for the tail items are based on the ratings in these clusters and for the head items on the ratings of individual items. If such partition and clustering are done properly, we show that this reduces the recommendation error rates for the tail items, while maintaining reasonable computational performance.