Abstract: Aiming at the problem of data sparsity in traditional collaborative filtering models,a collaborative filtering recommendation model based on trust model filling is proposed. The model gives emphasis to the trust attributes,and prefills the rating matrix by establishing trust model,in order to improve the data storage density. It obtains the similarity between items from the perspective of items and user attributes by similarity models. It coordinates the two types of similarity measurements by a self-adaptive coordination factor to gain final rating predictions of items. Experimental results,tested in different data sets,show that the newly proposed model can efficiently solve the problem of data sparsity in rating matrix,and provide better prediction accuracy of ratings involving an average improvement of 8% ,compared with traditional collaborative filtering models.

Key words: recommendation system, collaborative filtering, trust model, user attribute, similarity model, Mean Absolute Error(MAE)

摘要： 针对传统协同过滤模型中存在的数据稀疏性问题,提出一种基于信任模型填充的协同过滤推荐模型。对 信任属性进行研究,通过建立信任模型对评分矩阵进行预填充以提高数据存储密度,利用相似性模型分别从项目 和用户属性的角度度量项目相似性,通过自适应协调因子协调处理两方面的相似性度量结果,获得最终的项目预 测评分,基于不同的数据集进行实验验证,结果表明,在不同的数据集中,与传统的协同过滤模型相比,该模型能够 有效地处理评分矩阵的数据稀疏性问题,提高系统评分预测的准确度,平均改进程度为8% 。

关键词: 推荐系统, 协同过滤, 信任模型, 用户属性, 相似性模型, 平均绝对误差

CLC Number: 

• TP311