摘要： 为解决用户推荐过程中的数据稀疏性和冷启动问题,通过构建模糊时序分类模型设计相似度加权推荐算法。预处理数据时结合用户属性标签和时间维度建立模糊时序分类模型,并采用拉格朗日插值法进行空白数据的预测填充。针对不同用户个人评分偏高或偏低造成的评分差异以及单方面评级问题,利用相似度加权融合方法提高算法准确性。实验结果表明,该算法能有效降低平均绝对误差,提高推荐质量。

关键词: 模糊时序, 分类模型, 预测填充, 评分差异, 加权融合

Abstract: In order to solve the problem of data sparsity and cold start in the process of user recommendation,the similarity weighted recommendation algorithm is designed by constructing fuzzy time series classification model.A fuzzy time series classification model is established through combining user attribute label and time dimension while preprocessing data,and the blank data is predicted and filled by Lagrange interpolation.At the same time,aiming at the score difference caused by different users’ personal score too high or too low and unilateral rating problem,accuracy of the algorithm is promoted by similarity weighted fusion method.Experimental results show that the proposed algorithm can effectively reduce the Mean Absolute Error(MAE) and improves the recommendation quality.

Key words: fuzzy time series, classification model, predictive filling, score difference, weighted fusion

中图分类号: 

• TP391.1