微内容推荐路径优化的加速遗传算法研究

Research on Accelerating Genetic Algorithm of Micro-content Recommendation Path Optimization

指出随着互联网中以用户创造内容为源的微内容规模迅速增长,微内容的去中心化与碎片化等特性使网民获取信息的难度增加。针对微内容推荐同时受到用户主观偏好与用户感知行为影响这一特征,利用加速遗传算法对信息节点相似度的影响因素,从用户行为、内容偏好、社会网络关系三个方面进行有效融合,构建微内容推荐路径模型算法,并证明该算法的可行性和有效性。

The rapid growth of micro content created by users leads to the characteristics of micro content to decentration and fragmentation, which enable users to obtain information more difficult. According to the feature that the micro content recommendation is effected by user subjective preference and perceived behavior, this paper effectively fuses the influencing factors of information nodes similarity from three perspectives of user behavior, content preference and social network relations with the accelerating genetic algorithm. Finally, this paper develops a new model algorithm of micro-content recommendation path optimization based on it, and proves its feasibility and effectiveness.