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Research on collaborative recommendation of multidimensional sparse data based on personalised directional information fusion algorithm

机译：基于个性化方向信息融合算法的多维稀疏数据协作推荐研究

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摘要

In order to solve the problem of poor precision of recommendation results in the traditional collaborative recommendation method for multidimensional sparse data, a collaborative recommendation method for multidimensional sparse data based on personalised directional information fusion algorithm was proposed. The cosine similarity of the data vector was calculated and modified, and the score prediction set was established. On the basis of the predicted value and variable quantity value, the median, mean and model were used to populate and deconstruct the standard representative data, construct the multiple score matrix, solve the data location problem of sparse matrix, and realise the data collaborative recommendation. The experimental results show that the average error of the research method is about 0.03, lower than the traditional method, which proves that the method can effectively improve the accuracy of data recommendation.
机译：为了解决推荐的高精度的问题导致传统的多维稀疏数据的协作推荐方法，提出了一种基于个性化方向信息融合算法的多维稀疏数据的协作推荐方法。计算和修改数据矢量的余弦相似性，并建立得分预测集。在基于预测值和可变数量值的基础上，中位数，均值和模型用于填充和解构标准代表数据，构建多个分数矩阵，解决稀疏矩阵的数据定位问题，并实现数据协作推荐。实验结果表明，研究方法的平均误差约为0.03，低于传统方法，证明该方法可以有效提高数据建议的准确性。

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