计算机科学 ›› 2020, Vol. 47 ›› Issue (10): 97-101.doi: 10.11896/jsjkx.190700073
刘宇东, 孙豪, 蒋运承
LIU Yu-dong, SUN Hao, JIANG Yun-cheng
摘要: 微博的流行导致信息过载等问题日益突出,如何帮助用户快速而准确地找到需要的微博已成为亟待解决的问题。基于协同过滤技术和基于LDA的微博推荐虽然能够达到一定的准确性,但并不能解决内容分类过于笼统及使用LDA模型处理短文本存在弊端的问题。为此,文中提出了一种融合内容相似度与多特征计算的个性化微博推荐模型。首先,从微博内容语义出发,基于word2vec技术计算得到用户与微博的内容相似度;然后,根据微博的时间、点赞数、评论数和转发数等特征,计算微博的保鲜度及受欢迎度;最后,综合考虑微博的内容相似度、保鲜度和受欢迎度,计算微博排序评分,从而实现用户的个性化微博推荐。该模型根据内容相似度进行推荐,从而避免了上述问题,也使得推荐结果在语义上更为精确。实验结果表明,所提推荐模型在准确率、召回率和F值上均具有良好的表现,尤其在准确率方面有明显的提升效果,约提升了10%,F值也提升了约5%,从而证明了该模型的有效性。
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