计算机科学 ›› 2017, Vol. 44 ›› Issue (7): 180-184.doi: 10.11896/j.issn.1002-137X.2017.07.032
张晓阳,秦贵和,邹密,孙铭会,高庆洋
ZHANG Xiao-yang, QIN Gui-he, ZOU Mi, SUN Ming-hui and GAO Qing-yang
摘要： 随着网络的飞速发展,餐饮类的评价信息数量急剧增加。对餐饮评价进行有效分析不仅能够帮助消费者进行用餐选择,还可以帮助商家对餐厅服务进行改进。为此,提出了一种基于LDA(Latent Dirichlet Allocation)模型的餐厅推荐方法。首先,对餐厅评价信息进行情感分类,获取积极评价和好评率；其次,根据LDA模型对积极评价信息文本进行聚类,生成餐厅标签；最后,计算用户需求与餐厅标签的相似度,根据相似度和好评率向用户推荐餐厅。基于通过网络获取的真实餐饮评价信息进行实验,结果表明,该方法生成的餐厅标签的效果好,能准确地向用户推荐餐厅。
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