图书情报知识 ›› 2019, Vol. 0 ›› Issue (3): 81-90.doi: 10.13366/j.dik.2019.03.081

• 情报、信息与共享 • 上一篇    下一篇

用户饮食偏好挖掘及应用研究

岳子静,张颖怡,章成志   

  • 出版日期:2019-05-10 发布日期:2019-05-10

Mining and Application of Users’ Dietary Preferences

  • Online:2019-05-10 Published:2019-05-10

摘要:

[目的/意义]饮食是人类生存和发展的基本条件之一。挖掘用户的饮食偏好,能够解释不同用户在饮食习惯上的差异。用户饮食偏好挖掘方法的提出,能够拓宽饮食研究的路线。[研究设计/方法]以“大众点评”网站上的用户评论为数据源,结合情感分析,利用基于关键词的向量空间模型方法构建用户饮食偏好模型,在此基础上为网络用户推荐餐馆信息;并提出一种用户饮食偏好模型的评价方法以评估模型构建的效果。[结论/发现] 基于用户近期评论内容构建的饮食兴趣模型能够预测用户未来的饮食偏好。根据用户饮食偏好进行餐馆推荐,可在一定程度上为用户提供感兴趣、高质量的餐馆信息,满足用户个性化的饮食需求。[创新/价值]从情感分析的角度,基于用户生成内容挖掘用户饮食偏好,以满足用户的个性化饮食需求;提出的用户兴趣模型评价方法能够有效分析模型的有效性。

关键词: 饮食偏好挖掘, 评论挖掘, 情感分析, 用户兴趣建模, 信息推荐

Abstract:

[Purpose/Significance]Diet is one of the basic conditions for humansurvival and development. Exploring users' dietary preferences could explain their different dietary habits. Proposing a method for mining users' dietary preference could help broaden thedietary research routes. [Design/Methodology]Users' reviews on "www.dianping.com" were used as data sources. By using sentiment analysis and keyword based vector space model, this paper developed a users' dietary preference model, which could be used to recommend restaurants for users. Moreover, an evaluation methodwas proposed to assess the effectiveness of users' dietary preference models. [Findings/Conclusion]The model of users' dietary interests developed by their recent reviews could be used to predict their future dietary preference.Besides, recommending restaurant information based on users' dietary preferences can provide them with high quality information of their interests, and also meet their personalized dietary needs. [Originality/Value]This paper explores users' dietary preference according to user generated contents in order to meet their personalized favorites from the perspective of sentiment analysis. In addition, the proposed assessment method of users' interest model can effectively evaluate the validity of models.

Key words: Dietary preference mining, Review mining, Sentiment analysis, Modeling of users'  interests, Information recommendation