In this paper, we propose a skyline-based recommendation and ranking function. We suppose that some recommender systems, such as hotel recommender systems, are based not only on user preferences but also cost performance. For these kinds of applications, We first extract items with good cost performance and then identify items that users prefer, which reduce the computational cost of the online process. Based on the results of our preliminary experiments, we propose user feedback-based scoring and density-aware scoring methods where items that are highly similar to a user's latent requirements are recommended and attribute values in a dense area are quantized into a single value. The result of the experiments suggest that the density-aware scoring provides equal to or greater accuracy than the basic scoring.
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