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A multi-level collaborative filtering method that improves recommendations

机译:一种改进建议的多层次协作过滤方法

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Collaborative filtering is one of the most used approaches for providing recommendations in various online environments. Even though collaborative recommendation methods have been widely utilized due to their simplicity and ease of use, accuracy is still an issue. In this paper we propose a multi-level recommendation method with its main purpose being to assist users in decision making by providing recommendations of better quality. The proposed method can be applied in different online domains that use collaborative recommender systems, thus improving the overall user experience. The efficiency of the proposed method is shown by providing an extensive experimental evaluation using five real datasets and with comparisons to alternatives. (C) 2015 Elsevier Ltd. All rights reserved.
机译:协作过滤是在各种在线环境中提供建议的最常用方法之一。尽管协作推荐方法由于其简单性和易用性而被广泛使用,但准确性仍然是一个问题。在本文中,我们提出了一种多层次的推荐方法,其主要目的是通过提供质量更好的推荐来帮助用户进行决策。所提出的方法可以应用于使用协作推荐系统的不同在线域中,从而改善整体用户体验。通过使用五个真实数据集并与替代方法进行比较,提供了广泛的实验评估,从而证明了所提出方法的效率。 (C)2015 Elsevier Ltd.保留所有权利。

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