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The Effect of Neighborhood Selection on Collaborative Filtering and a Novel Hybrid Algorithm

机译：邻域选择对协同过滤的影响及新型混合算法

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摘要

Recommender systems are widely used in industry and are still active research areas in academia. For many businesses, they have become indispensable business tools. Producing accurate results for such systems is important for the operations of the businesses. For this reason, various algorithms and approaches have been developed for recommender systems to increase the prediction accuracy. Collaborative filtering is one of the most successful approaches. In collaborative filtering, in order to predict more accurately, it is recommended to determine user's active neighbors. k-nearest neighbor (k-NN) algorithm is one of the most widely used neighbor selection algorithms. However, k-NN algorithm uses a fixed k value that reduces the accuracy of the prediction. In this paper, we present two novel approaches to increase the prediction accuracy of recommender systems; k%-nearest neighbor (k%-NN) algorithm to determine the appropriate k value for a user and a hybrid algorithm that combines a collaborative filtering technique and content-based approach. Our test results demonstrate that k%-NN algorithm increases the average prediction accuracy compared to the traditional k-NN algorithm. Additionally, when the proposed hybrid algorithm is used with k%-NN, it produces more accurate results than the conventional collaborative filtering technique and content-based approach.
机译：推荐系统广泛用于工业中，并且仍然是学术界活跃的研究领域。对于许多企业而言，它们已成为必不可少的商业工具。为此类系统产生准确的结果对于企业的运营至关重要。由于这个原因，已经为推荐系统开发了各种算法和方法以提高预测精度。协作过滤是最成功的方法之一。在协同过滤中，为了更准确地进行预测，建议确定用户的活动邻居。 k最近邻居（k-NN）算法是使用最广泛的邻居选择算法之一。但是，k-NN算法使用固定的k值，这会降低预测的准确性。在本文中，我们提出了两种新颖的方法来提高推荐系统的预测准确性。可为用户确定合适的k值的k％最近邻居（k％-NN）算法和结合了协作过滤技术和基于内容的方法的混合算法。我们的测试结果表明，与传统的k-NN算法相比，k％-NN算法提高了平均预测精度。另外，当所提出的混合算法与k％-NN一起使用时，它会比传统的协作过滤技术和基于内容的方法产生更准确的结果。

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