基于用户偏好优化模型的推荐算法研究

传统的个性化推荐算法普遍存在数据稀疏性问题，影响了推荐的准确度。Slope one算法具有简单、高效等特点，但该算法只是根据用户—项目评分矩阵进行数据分析，对所有用户采用一致性的权重进行计算，忽视了用户对项目类型的喜好程度。针对上述问题进行了研究，提出LR-Slope one算法。首先根据用户—项目评分矩阵和项目类型信息构建用户对项目类型的偏好矩阵；然后利用线性回归模型计算用户对每个类型的权重，采用随机梯度下降算法优化权重；最后结合Slope one算法预测评分，填充评分矩阵，提高推荐的质量。实验结果表明，所提算法提高了推荐的精度，有效缓解了稀疏性问题。

Research on recommendation algorithm based on user preference optimization model

Traditional personalized recommendation algorithm generally suffers from the problem of data sparseness, which affects the accuracy of recommendation. The Slope one algorithm is simple and efficient, but the algorithm is only based on the user - project score matrix to analyze the data, ignoring the type characteristics of the project and the user''s preference for the type of the project. In order to solve the above problems, this paper proposed the LR-Slope one algorithm. Firstly, it constructed the user''s preference matrix based on user project score matrix and project type information. Secondly, it calculated the weight of each type by linear regression model and optimized the weight by random gradient descending algorithm. Finally, it predicted the score combined by Slope one, filled the scoring matrix, which improved the quality of recommendation. Experimental results show that the proposed algorithm improves the accuracy of recommendation and alleviates data sparseness effectively.