Niche Product Retrieval in Top-N Recommendation

A challenge for personalised recommender systems is to target products in the long tail. That is, to recommend products that the end-user likes, but that are not generally popular. To achieve this goal, in this paper we propose two strategies to identify relevant but niche products. The first strategy computes an inverse item popularity and applies it during the steps of top-N recommendation. Given a prior probability distribution of relevance based on item popularity, and a user-specific relevance probability, the other strategy uses a number of scores based on distance measures between these two distributions. We emphasize that the problem is to recommend relevant items from the user's broader range of tastes. Hence, in evaluation a concentration index is calculated to measure the extent to which the recommendation is spread to the user's niche tastes in conjunction with the standard precision metric which measures the overall relevance of the recommended set. The methods are evaluated empirically using the Movielens dataset and show a strong performance in niche item retrieval at the cost of a small reduction in precision.

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