A recommender system based on collaborative filtering using ontology and dimensionality reduction techniques
A recommender system based on collaborative filtering using ontology and dimensionality reduction techniques
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DOI：
10.1016/j.eswa.2017.09.058
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发表时间：
2018-02-01
影响因子：
8.5
通讯作者：
Bagherifard, Karamollah
中科院分区：
文献类型：
--
作者：
Nilashi, Mehrbakhsh;Ibrahim, Othman;Bagherifard, Karamollah
提高方法的效率一直是推荐系统面临的一大挑战。考虑推荐系统在推荐项目时的准确性和计算时间之间的权衡也很重要，因为它们需要准确而实时地产生推荐。为此，本研究提出了一种基于协同过滤（CF）方法的混合推荐方法。因此，在本研究中，我们利用降维和本体技术解决了推荐系统的两个主要缺点：稀疏性和可扩展性。然后，我们在CF部分使用本体来提高推荐的准确性。在CF部分，我们还使用了一种降维技术奇异值分解（SVD），在每个项目和用户簇中找到最相似的项目和用户，这可以显著提高推荐方法的可扩展性。我们在两个真实世界的数据集上评估了该方法，以显示其有效性，并将结果与文献中方法的结果进行了比较。结果表明，我们的方法有效地改善了CF. (C) 2017 Elsevier Ltd.的稀疏性和可扩展性问题。版权所有。
Improving the efficiency of methods has been a big challenge in recommender systems. It has been also important to consider the trade-off between the accuracy and the computation time in recommending the items by the recommender systems as they need to produce the recommendations accurately and meanwhile in real-time. In this regard, this research develops a new hybrid recommendation method based on Collaborative Filtering (CF) approaches. Accordingly, in this research we solve two main drawbacks of recommender systems, sparsity and scalability, using dimensionality reduction and ontology techniques. Then, we use ontology to improve the accuracy of recommendations in CF part. In the CF part, we also use a dimensionality reduction technique, Singular Value Decomposition (SVD), to find the most similar items and users in each cluster of items and users which can significantly improve the scalability of the recommendation method. We evaluate the method on two real-world datasets to show its effectiveness and compare the results with the results of methods in the literature. The results showed that our method is effective in improving the sparsity and scalability problems in CF. (C) 2017 Elsevier Ltd. All rights reserved.