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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。
It is pointed out that with the rapid growth of the content of micro-content in the internet based on user-generated content, the decenterization and fragmentation of micro-content have made it more and more difficult for netizens to obtain information. Aiming at the feature that the recommendation of micro content is affected by subjective preference and user perception at the same time, this paper uses the accelerated genetic algorithm to influence the similarity of information nodes, and integrates effectively the three aspects of user behavior, content preference and social network. The content recommendation path model algorithm, and prove the feasibility and effectiveness of the algorithm.