一种多层多维的关联规则挖掘算法在推荐系统中的应用

针对传统协同过滤算法将用户-项目评分矩阵作为数据的输入，试图找到最相似的用户或者项目，但却忽略了用户与项目属性之间的关联关系等问题，本文构建一种多层数据的模型，在不同层次之间找出多维序列，挖掘出频繁多维序列模式，输出关联规则。用输出的关联规则改进评分矩阵，改进后的数据包含了用户和项目之间的关联关系，经过协同过滤算法输出TOP-N推荐项目。在MovieLens数据集上进行试验验证，实验结果表明所提方法能够优化模型的推荐性能。

Application of Multi-layer Multi-dimensional Association #br# Rule Mining Algorithm in Recommendation System

 The traditional collaborative filtering algorithm uses the user-item scoring matrix as the input of data to try to find the most similar users or projects. This method ignores the intrinsic link between the user and the project. Aiming at the above problems, this paper proposes a model construction of multi-layer data, which finds multi-dimensional sequences between different levels, mines frequent multi-dimensional sequence patterns, and outputs association rules. The score matrix is improved by the output association rule. The improved data contains the relationship between the user and the project, and the TOP-N recommendation item is output through the collaborative filtering algorithm. The experimental results on MovieLens dataset show that the proposed method can optimize the recommended performance of the model.