摘要
根据林产品贸易信息的特点,利用一种复合关键词向量空间模型来表示林产品贸易信息网站的用户兴趣模型:向量空间中的每一个复合关键词包括供求分类、林产品名称和产地三个关键词以及林产品的规格、价格范围和公司名称集合等信息,每一个复合关键词均拥有一个用户的感兴趣度值。利用用户的浏览、注册、发布信息等行为以及引入兴趣度值的遗忘因子,为用户兴趣模型提供了学习和更新方法。通过引入用户的短期兴趣集合,使得兴趣模型得以体现用户的长期兴趣和短期兴趣。最后,基于此用户兴趣模型给出了基于内容的推荐算法,并通过对比实验阐明了其优势。
Based on the features of the forest products trading information, a compound keywords vector space model was utilized to represent the user interest model of the forest products trading information website. Every compound keyword in the vector space consists of three keywords, which are supply and demand classification, the name of the forest product and the production place, and some information about the specifications, price and the company name of the forest products. Every compound keyword has a user interest value. The learning and updating of the user interest model were realized through the browsing, registering and releasing behaviors of the users and introducing the forgetting factors for the interest values, respectively. The persistent interests and the temporary interests of the users were distinguished by introducing a temporary interests group. Finally, the recommendation algorithms based on the contents filtering were proposed based on our user interest model, and its advantage was demonstrated by comparison experiments.
出处
《计算机工程与科学》
CSCD
北大核心
2013年第5期154-160,共7页
Computer Engineering & Science
基金
中央高校基本科研业务费专项资金资助项目(BLX200928)
关键词
林产品
贸易信息网站
用户兴趣建模
复合关键词
向量空间模型
forest product
trading information website
user interest model
compound keywords
vector space model
