基于多模网络模型的个性化信息推荐算法及应用

Personalized Information Recommendation Algorithm Based on Multimode Network Model and its Application

为了获得更高的信息推荐效率和更精确的推荐结果,结合k-means聚类法设计了信息推荐算法。以网络信息资源的属性及整体变化的相似度判断为基础进行信息检索,通过用户对资源的偏好程度构建多模网络模型,采用k-means聚类法建立最近邻用户集,以综合特征值为聚类起点对推荐的信息资源进行排序,从而实现信息资源的推荐。将该算法应用到就业信息推荐平台中,结果表明所提出的算法达到了较高的性能指标,对于各种创业、就业网络平台具有很强的实用性。

In order to obtain high information recommendation efficiency and accurate recommendation results,an information recommendation algorithm is designed by k-means clustering method.Information retrieval is based on the attribute of network information resources and the similarity judgment of overall changes.A multi-mode network model is constructed through the user's preference for resources.The nearest neighbor user set is established by the k-means clustering method,and the recommended information resources are sorted with the comprehensive eigenvalue as the clustering starting point,so as to realize the recommendation of information resources.The algorithm is applied to the employment information recommendation platform.The results show that the proposed algorithm achieves high performance index and has strong practicability for various entrepreneurship and employment network platforms.