一种2阶段联合预测的协同过滤推荐算法

Research on a Collaborative Filtering Recommendation Algorithm Based on Two-stage Joint Prediction

传统协同过滤推荐算法存在评分数据稀疏性、用户评分偏好缺失性、传统相似性度量局限性的问题.提出一种基于物品预测得分与基于用户偏好得分的2阶段联合推荐算法:第1阶段,使用基于物品的预测得分补全评分矩阵,利用时间权重因子改进物品相似度;第2阶段,先利用评分偏好模型将完整的评分矩阵转化为针对评分类别的用户评分偏好矩阵,再通过该矩阵使用基于用户的协同过滤算法计算偏好得分,利用用户共同评分数权重改进用户相似度,最后将基于物品的预测得分联合基于用户的偏好得分作为目标用户的综合预测评分.实验结果表明,在不同近邻用户数和不同推荐列表长度下,该算法的准确率和召回率均优于传统协同过滤算法,且针对不同稀疏度数据集,该算法的MAE增量值降低了8%~24.6%,具有更高的推荐精度和准确度.

Traditional collaborative filtering recommendation algorithm has some problems,such as the sparsity of rating data,the lack of user rating preference,and the limitation of traditional similarity measurement.In this paper,a two-stage recommendation model combining item prediction score and user preference score is proposed.In the first stage,the item-based prediction score is used to complete the score matrix,and the time weight factor is used to improve the item similarity;In the second stage,the complete scoring matrix is transformed into a user scoring preference matrix for scoring categories by using the scoring preference model,then the preference score is calculated by using the user-based collaborative filtering algorithm through the matrix,and the user common rating score weight is used to improve the user similarity.Finally,the item-based prediction score and the user-based preference score are used as the comprehensive prediction score of the target user.Experimental results show that the proposed algorithm outperforms the traditional collaborative filtering algorithm in terms of accuracy and recall rate under different number of neighbor users and different lengths of recommendation list.Moreover,for different sparsity data sets,the MAEincrement value of the proposed algorithm is reduced by 8%-24.6%,with higher recommendation precision and accuracy.