基于项目模糊相似度的协同过滤推荐算法  

Collaborative filtering recommendation algorithm based on item fuzzy similarity

作　　者：王森 陈莉[1] 张洁 Wang Sen;Chen Li;Zhang Jie(School of Information Science&Technology,Northwest University,Xi’an 710127,China)

机构地区：[1]西北大学信息科学与技术学院,西安710127

出　　处：《计算机应用研究》2021年第3期696-701,共6页Application Research of Computers

摘　　要：针对传统协同过滤算法中评分和标签存在的模糊性问题进行了研究,利用梯形模糊数描述评分与满意度的映射关系,在考虑评分稀疏性的基础上构建了一种新的梯形模糊评分模型以判断基于模糊评分的相似度,分析标签与项目的隶属度,构建模糊项目标签矩阵以衡量基于标签隶属度的相似度,最终采用改进的评分预测策略进行评分估计。在MovieLens数据集上的实验结果显示,所提算法在抑制项目冷启动、缓解模糊性和稀疏性问题的同时,提高了预测精度,表明了该算法的有效性。In view of the problem of fuzziness of rating and tag in traditional collaborative filtering algorithms,this paper used trapezoidal fuzzy number to describe the mapping relationship between rating and satisfaction.The algorithm considered the impact of sparseness of the rating,constructed a new trapezoidal fuzzy rating model to determine the similarity based on fuzzy ra-ting,analyzed the degree of membership between the tag and the item,and constructed a fuzzy item-tag matrix to measure the similarity based on the degree of tag membership.Finally,it used the improved scoring prediction strategy to estimate the score.The experimental results on the MovieLens dataset show that the proposed algorithm improves the prediction accuracy while suppressing the cold start of the project,alleviating the problems of fuzziness and sparseness,which indicates the effectiveness of the proposed algorithm.

关 键 词：协同过滤 模糊相似度 梯形模糊评分模型 模糊项目标签矩阵 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]