摘要
提出了一种考虑平均偏好权重的协同过滤个性化推荐算法。该算法分为邻域计算、数据集划分、偏好预测3个阶段。在邻域计算阶段,采用基于欧氏距离的KNN来确定邻域;同时对数据集按照其本身特点设定的流行度阈值进行划分;在预测评分时,对已有的邻域按照流行度选取部分项目,基于项目集的偏好相似度求解用户的平均偏好权重,据此对用户进行先后两次预测,再求平均结果。在Movielens 100K数据集上将所提算法与典型的余弦推荐算法、person推荐算法、基于项目偏好的协调过滤算法和用户属性加权活跃近邻的协同过滤算法进行比较实验,结果表明新算法在MAE上表现的更优秀。
This paper presented a new recommendation algorithm which takes into account the average preference weight.The algorithm is divided into three stages：neighborhood computing,data set partitioning and preference prediction.In the neighborhood calculation,the KNN based on the Euclidean distance is used to determine the neighborhood.At the same time,the data set is divided into the data set and the non-popular data set according to the popularity threshold of the data set itself.When the score is predicted,the existing neighborhood selects part of the project according to the popularity degree,and predicts the user＇s average preference weight based on the preference similarity of the item set.The results show that on the Movielens 100 Kdata set,the new algorithm is superior to the typical cosine recommendation algorithm,the person recommendation algorithm,the collaborative filtering algorithm based on the project preference coordination filtering algorithm and the user attribute weighted active neighbor existing algorithms in MAE.
作者
何佶星
陈汶滨
牟斌皓
HE Ji -xing ,CHEN Wen- bin ,MOU Bin -hao(School of Computer Science, Southwest Petroleum University, Chengdu 610500, Chin)
出处
《计算机科学》
CSCD
北大核心
2018年第B06期493-496,共4页
Computer Science
关键词
协同过滤
KNN
个性化推荐算法
流行度划分
平均偏好权重
邻域计算
Coordination filtering
KNN
Personalized recommended algorithm
Popularity division
Average preference weight
Neighborhood calculation
