基于多模网络模型的个性化信息推荐算法及应用

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。

基于个性化多模网络模型的音乐推荐技术

为了获得更高的推荐效率和更精确的推荐结果,提出了一种基于个性化多模网络模型的音乐推荐技术。以音乐音高的轮廓及整体变化的相似度判断为基础进行音乐信息检索,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音乐资源进行排序,从而实现个性化音乐的推荐。实验结果表明,所提出的算法达到了较高的性能指标,对于各种音乐播放软件具有很强的实用性。本文提出的推荐技术,采用了个性化多模网络模型,对于其他算法来说具有一定的创新,因此,该技术对于海量音乐资源的处理及推荐具有一定的实用价值。

Consensus of Multi-Agent Systems with Prestissimo Scale-Free Networks

In this paper, the relations of the network topology and the moving consensus of multi-agent systems are studied. A consensus-prestissimo scale-free network model with the static preferential-consensus attachment is presented on the rewired link of the regular network. The effects of the static preferential-consensus BA network on the algebraic connectivity of the topology graph are compared with the regular network. The robustness gain to delay is analyzed for variable network topology with the same scale. The time to reach the consensus is studied for the dynamic network with and without communication delays. By applying the computer simulations, it is validated that the speed of the convergence of multi-agent systems can be greatly improved in the preferential-consensus BA network model with different configuration.