一种基于局部近邻Slope One协同过滤推荐算法

经典的Slope One算法采用线性回归模型对目标项目进行预测评分,但在项目评分偏差表构建过程中产生了部分噪声数据,影响了算法的推荐性能。为了解决该问题,建立了一种基于局部近邻Slope One协同过滤推荐算法。算法计算了当前活跃用户针对不同推荐商品的近邻用户集,其邻居用户集根据目标项目的不同而动态变化;根据活跃用户关于不同目标项目的邻居用户数据来进一步优化项目之间的平均偏差,进而产生推荐。对比实验说明,该算法在MovieLens数据集上具有较高推荐精度。

A Slope One collaborative filtering recommendation algorithm based on local nearest neighbors

The classical Slope One algorithm employs the linear regression model to generate recommendation. However, the recommendation suffers from decreased precision due to the noise data emerged in item score deviation table construction. We propose a Slope One collaborative filtering recommendation algorithm to solve this problem based on local nearest neighbors. Neighbor users set dynamically change along with the change of the target item by calculating target users based on the neighbor users of different target items. The average deviation between items is further optimized and recommendation is generated according to the neighbor user data of different target items. Experimental results on MovieLens dataset show that the improved algorithm can promote the prediction accuracy of the recommendation.