摘要
协同过滤推荐是目前应用最为广泛的推荐策略之一,但存在数据稀疏和难扩展问题.文中在传统基于用户的协同过滤推荐算法的基础上,引入信任关系计算,利用信任关系的条件传递特性,设计并构建一个集用户声望信任和用户局部信任的混和信任网络,并将用户间评分相似度和网络中用户间信任评价度结合,为用户寻找更多基于信任因素和兴趣因素的二维相似近邻.在Epinions数据集上以平均绝对误差(MAE)和均方根误差(RSME)等作为实验评价指标,对该方法进行验证实验.结果表明相比传统协同过滤推荐算法,该方法在MAE上提高约6.8%,最优值达到0.7513,t检验的结果也表明该方法能显著提高推荐系统性能.
Collaborative filteration is one of the most widely used recommendation strategies, in which data sparsity problem and expansion difficulty exist. Based on traditional user-based collaborative filtering algorithms, the trust computation is introduced into the process of recommendation. Making full use of the propagation characteristics of trust relationship under some conditions, a hybrid network composed of the user reputation-trust and the user local-trust is designed and built. And the user rating similarity is combined with trust evaluation of the hybrid network, which helps users to discover more two-dimensional similarity neighbors based on trust and interest factors. The proposed method is validated by the experiment on Epinions dataset with Mean Absolute Error （ MAE） and Root Mean Square Error （ RSME） as the evaluation index. The results show that compared to the traditional collaborative filtering recommendation algorithms, MAE of the proposed method increases about 6. 8% and the optimal value reaches 0 . 7513 , and the t-test results also show that the proposed method improves the performance significantly.
出处
《模式识别与人工智能》
EI
CSCD
北大核心
2014年第5期417-425,共9页
Pattern Recognition and Artificial Intelligence
基金
国家自然科学基金项目(No.60803086)
国家科技支撑计划子课题(No.2013BAH21B02-01)
北京市自然科学基金项目(No.4123091)
北京市属高等学校人才强教深化计划"中青年骨干人才培养计划"项目(No.PHR20110815)资助
关键词
协同过滤
信任计算
推荐系统
Collaborative Filtration
Trust Computation
Recommendation System
