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。
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.
作者
孙连
李书琴
刘斌
SUN Lian;LI Shuqin;LIU Bin(College of Information Engineering,Northwest A&F University,Xianyang,Shaanxi 712100,China)
出处
《计算机工程》
CAS
CSCD
北大核心
2019年第10期196-202,共7页
Computer Engineering
基金
中国博士后科学基金(2017M613216)
陕西省自然科学基金面上项目(2017JM6059)
陕西省博士后基金(2016BSHEDZZ121)
陕西省重点研发计划(2017GY-197)
