摘要
基于社交网络的社会化推荐的依据是社交网络中亲密关系的用户往往具有相似的兴趣爱好。当前基于社交网络的推荐没有充分地利用社交关系信息,导致预测精度较低和计算效率低、收敛速度慢。为了缓解数据稀疏以及提高推荐系统准确性,对社交网络提出了一种融合用户熟悉度和用户特征评分的推荐模型,充分利用了社交网络中的用户关系信息和特征信息。实验表明,给出的推荐算法相比传统的协同过滤算法明显提高了推荐精度。
The basis of social recommendation based on social networks is that users Of intimate relationships in social networks often have similar interests. Existing recommender approaches based on social trust relationships have not fully utilized such relationships and thus have low prediction accuracy or slow convergence speed. In order to improve the system accuracy and reduce data sparseness, we propose an improved recommendation model which blends user intimate rating and user characteristic rating and fully utilizes social relationships and characteristic information. The experiments show that the proposed algorithm is significantly improved compared with the traditional collaborative filtering algorithm.
出处
《计算机时代》
2015年第8期29-30,33,共3页
Computer Era
关键词
社交网络
推荐系统
用户熟悉度
用户特征评分
评分融合
social network
recommendation system
user intimate rating
user characteristic rating
rating blending
