欢迎您！东篱公司
退出
-
申报数据库
-
立项数据库
-
成果数据库
-
项目获奖数据库
中文摘要：
信任网络能模拟现实社会，因此其用户间的信任数据可用于推荐算法，但同时也面临数据稀疏的问题，推荐效果较差。针对该问题，提出融合标签传播和信任扩散的个性化推荐方法。设计基于标签传播的大社区发现算法，得到独属于每个用户的大社区。根据各用户所属大社区内用户间的信任网络，给出信任预处理算法，预测用户新的信任关系，从而扩展用户的信任网络，并利用混合信任扩散算法，使用户及其所在大社区内其他用户之间的信任度更趋差异化。使用Epinions. com上的数据集进行实验，结果表明，与普通信任网络推荐方法相比，该方法的推荐准确度有明显提高。
英文摘要：
The trust network can simulate the real society prominently,so the trust data can be used in the recommend algorithm. However,the trust data is faced with the problem of sparse data,and its recommendation result is undesirable. Aiming at this problem,this paper proposes a personalized recommendation method fused with label propagation and trust diffusion. The community discovery algorithm based on label propagation is proposed to discover the big community which belongs to each single user. According to the trust network of each single user,the preprocess algorithm is proposed to predict the new trust relationship so as to extend the trust-aware network. The hybrid trust diffusion algorithm is proposed to make distinct difference in the trust degree between one single user and other users in the big community. The experiment uses the dataset in Epinions. com and the result shows that the presented method has distinct improvement in accuracy compared with the normal trust recommendation methods.
同期刊论文项目
同项目期刊论文
期刊信息
位置：