王冉, 徐怡, 胡善忠, 何明慧. 基于加权二部图的Slope One推荐算法[J]. 微电子学与计算机, 2018, 35(3): 93-98.
引用本文: 王冉, 徐怡, 胡善忠, 何明慧. 基于加权二部图的Slope One推荐算法[J]. 微电子学与计算机, 2018, 35(3): 93-98.
WANG Ran, XU Yi, HU Shan-zhong, HE Ming-hui. Slope One Recommendation Algorithm Based on Weighted Bipartite Graph[J]. Microelectronics & Computer, 2018, 35(3): 93-98.
Citation: WANG Ran, XU Yi, HU Shan-zhong, HE Ming-hui. Slope One Recommendation Algorithm Based on Weighted Bipartite Graph[J]. Microelectronics & Computer, 2018, 35(3): 93-98.

基于加权二部图的Slope One推荐算法

Slope One Recommendation Algorithm Based on Weighted Bipartite Graph

  • 摘要: 相对协同过滤算法, Slope One算法在执行速度上更加迅速, 并且易于实现.但是算法没有考虑项目之间的推荐关系, 故提出了一种基于加权二部图的Slope One推荐算法.利用加权二部图推荐算法计算项目之间的推荐程度, 并用计算得到的项目之间的推荐程度对Slope One预测评分过程进行加权处理, 由于二部图推荐算法计算得到的项目之间的推荐程度是非对称的, 从而使推荐结果更加多样化.在MovieLens数据集上利用5-折交叉验证以及4种评价指标对算法进行验证, 实验表明改进后的算法在提高了推荐准确性的同时也提高了推荐的多样性.

     

    Abstract: Compared with the collaborative filtering algorithm, the Slope One algorithm is faster and easy to implement, however, the algorithm does not consider the recommended level between items. Therefore, this paper proposes a Slope One recommendation algorithm based on weighted bipartite graph. The weighted bipartite graph algorithm calculates the recommended level between the items and uses the recommended level to weight the prediction of the Slope One. Since the recommended level between the items calculated by the bipartite graph recommendation algorithm is asymmetric, the results are more diversified.We test the proposed method by 5-fold cross-validation on the MovieLens with four metrics: ranking score, Recall, Precision and diversification, and the experimental results show that the improved algorithm improves the recommendation accuracy and improves the recommendation diversity.

     

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