计算机工程与应用2019，Vol.55Issue(20)：114-121,8.DOI:10.3778/j.issn.1002-8331.1807-0043
融合潜在社交信任模型的协同过滤推荐
Collaborative Filtering Recommendation Integrating Potential Social Trust Model
吴航 1江红1
作者信息
- 1. 华东师范大学 计算机科学与软件工程学院,上海 200062
- 折叠
摘要
Abstract
The recommendation system plays a significant role in dealing with the information overload problem, but the recommendation system also has its disadvantages in terms of its data sparsity and cold start problems. Using traditional collaborative filtering algorithms can no longer satisfy the technical development of the recommendation system. With the development of social networks, the trust relationship of friends has been widely used in the recommendation system. However, in real life, the trust relationship in social networks also has the problem of sparse data. In order to better improve the quality of recommendations, a collaborative filtering recommendation algorithm for integrating potential social trust models is proposed. The new social trust model is mainly composed of the following parts:global trust values and expert models in the social matrix, an improved trust propagation model, an improved Pearson coefficient model. Through the analysis of the experimental results, it is known that the recommended algorithm for the fusion of the new model helps to improve the recommendation effect.关键词
推荐系统/协同过滤/社交网络/信任模型Key words
recommendation system/collaborative filtering/social network/trust model分类
信息技术与安全科学引用本文复制引用
吴航,江红..融合潜在社交信任模型的协同过滤推荐[J].计算机工程与应用,2019,55(20):114-121,8.
