摘要： 针对加权LeaderRank算法存在的权值均分、主题漂移等问题，提出一种用户社交网络排序算法。结合GloVe模型、余弦相似度计算方法和牛顿冷却定律，通过引入链入链出因子、主题相关度因子和时间衰减度因子，改善加权LeaderRank算法的不足。实验结果表明，与加权LeaderRank算法相比，该算法的精确率、点击率和NDCG值分别提高7.80%、6.73%和4.75%，可有效提高排序质量。

关键词: 加权LeaderRank算法, 链入链出因子, 主题相关度因子, 时间衰减度因子, GloVe模型

Abstract: To address the problems of the average weight distribution and topic drift in the weighted LeaderRank algorithm,a user social network sorting algorithm is proposed.Integrating the GloVe model,cosine similarity calculation method and Newton's law of cooling,introduce the link-in and link-out factor,the topic relevance factor and the time attenuation factor to the weighted LeaderRank algorithm to improve its disadvantages.Experimental results show that compared with the weighted LeaderRank algorithm,the precision,the click rate and the NDCG value of the proposed algorithm is increased by 7.80%,6.73% and 4.75% respectively.The sorting quality can be improved effectively.

Key words: weighted LeaderRank algorithm, link-in and link-out factors, topic relevance factor, time attenuation factor, GloVe model

中图分类号: 

• TP391