基于网络信息的混合推荐算法研究

Research on Hybrid Recommendation Algorithm Based on Network Information

为解决因网络信息严重过载而导致用户获取有效信息困难的问题,笔者提出一种混合式网络信息推荐算法。首先为每个用户建立主题模型,同时应用该算法结合牛顿冷却定率平衡时间因素对用户偏好所产生的影响进行分析,再分别通过改进的协同过滤方法和基于内容的推荐方法满足用户对信息的多样性和个性化的需求。通过实践证明,该算法在推荐的准确率和召回率方面表现良好,对用户偏好的预测效果良好,是有效的推荐方法。

In order to solve the problem that obtaining effective information is difficult for users due to serious network information overload,a new method of network information recommendation is proposed. Firstly,a topic model for each user is built,the establishment of theme model which combines Newton cooling rate is to balance the effect of the time factor on the user interest. Then,collaborative filtering algorithm and content-based recommendation algorithm are used to meet the user’s needs for information diversity and personalization. The experimental results show that the algorithm can effectively improve the accuracy and recall of recommendation,and the prediction of user preference effect is good. It is an effective recommendation method.