融合MHS与AIM-RT的谱聚类优化推荐算法

传统协同过滤推荐算法存在时序性过低以及用户过多时数据稀疏相似用户计算复杂度高等问题。针对这些问题，提出融合最小哈希签名（MHS）与时序模型预测（AIM-RT）的谱聚类优化推荐算法。首先使用MHS与Levenshtein距离测度对用户—项目评分矩阵提取相似用户；然后利用时序模型进行权重拟合的AIM-RT预测算法预测评分补全相似用户稀疏矩阵；最后结合谱聚类进行相似用户优化，找到最优相似用户集合完成最终推荐。通过实验分析验证表明，所提推荐算法能够在计算复杂度、评分预测精度、数据缺失填补等方面提高整体推荐性能。

Spectral clustering optimization recommendation algorithm combining MHS and AIM-RT

The traditional collaborative filtering recommendation algorithm has the problems of low timing and high computational complexity of similar users with sparse data when there are too many users. According to the above problem, this paper proposed a spectral clustering optimization recommendation algorithm combining the minimum hash signature and temporal model prediction. Firstly, it used the MHS and Levenshtein distance measure to extract similar users from user-item scoring matrix. Then it used AIM-RT prediction algorithm with weight fitting to complete sparse matrix of similar users. Finally, it carried similar user optimization out by combining spectral clustering to find the optimal set of similar users and completed the final recommendation. Experimental analysis and verification show that the proposed recommendation algorithm can improve the overall recommendation performance in terms of computational complexity, scoring prediction accuracy, data missing filling and other aspects.