基于改进位置社交的近邻知识推荐算法

Neighbour Knowledge Recommendation Algorithm Based on Improved Location-based Social Network

信息过载是当前各类网络中存在的普遍问题,社交网络中通过推荐算法为用户推荐感兴趣的内容,但该类算法并不适用于学习网络中存在特定逻辑联系的知识点推荐。结合社交网络及LBSN网络中的兴趣点推荐算法,提出了一种面向学习网络相关知识点的改进LBSN推荐算法,通过学习网络中的相似用户计算及知识路径发现,为用户推荐当前学习相关的近邻知识点,并通过实验数据证明了学习网络中加入学习推荐对学习者效率及学习质量提升的效果。

Information overload is a common problem in all kinds of networks.Recommendation algorithm is used to recom mend content in social networks what user interest,b ut this algorithm is not suitable for learning knowledge points with specific logi cal connections in learning network.Based on the recommendation algorithm in social network and the LBSN network,an improved LBSN learning oriented network recommendation algorithm is proposed,through the calculation by user similarity and the current path discovery,recommending an associated knowledge point for the user.The experimental data proves that the addition of learn ing recommendation in learning network can effect the learning efficiency and improve the quality of learning.