基于用户类别偏好相似度和联合矩阵分解的推荐算法

摘要

利用上下文信息来提高推荐准确率并增强用户体验是当前推荐领域研究热点之一,然而现有的上下文感知推荐算法依然面临数据稀疏性问题的挑战.为了进一步缓解数据稀疏性问题,本文提出一种基于用户类别偏好相似度及联合矩阵分解的推荐算法(Joint matrix factorization with user category preference,JM F-UCP),它结合用户评分数据及用户类别偏好进行物品推荐,以解决用户评分数据稀疏时评分预测准确率低的问题.算法的时间复杂度随着数据量的增加呈线性增长,因此适用于大规模数据.通过在真实数据集Movielens上的实验结果表明,本文提出的方法在RMSE评价指标上优于现有代表性的算法,验证了本文所提出的推荐算法的有效性.%Using context information to improve the accuracy of recommendation systems and enhance us-er experience is one of the hottest topics in the domain of recommend systems.However the issue of data sparse still challenges the existing context-aware recommender system.To better alleviate the data sparse problem,this paper proposes a rating prediction method,i.e.,joint matrix factorization with user category prefernce(JM F-UCP).Based on the joint matrix factorization,the method addresses the data sparse problem by combining user′s rating information and user category preference to predict the rating score with higher accuracy.The time complexity of the proposed method linearly increases with the num-ber of amount of dataset and is scalable to very large datasets.Experimental results on real world rating dataset MovieLens demonstrate that the proposed method can achieve better accuracy.