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Recommending items ranked by popularity has been found to be a fairly competitive approach in the top-N recommendation task. In this paper we explore whether popularity should always be expected to be an effective approach for recommendation, and what causes, factors and conditions determine and explain such effectiveness. We focus on two fundamental(More)
We develop a probabilistic formulation giving rise to a formal version of heuristic k nearest-neighbor (kNN) collaborative filtering. Different independence assumptions in our scheme lead to user-based, item-based, normalized and non-normalized variants that match in structure the traditional formulations, while showing equivalent empirical effectiveness.(More)
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