摘要
识别用户偏好漂移是维护用户偏好模式、确保偏好描述准确的关键之一,随着移动商务的迅猛发展,近年来越来越受到重视。一个研究方向是基于聚类实现偏好漂移的识别,但目前研究对于资源对象间多元的弱关联处理存在不足,为此本文结合情境化推荐的特征,构建了情境化资源的超图模型,在对资源相似度、资源簇相似度、用户偏好漂移度等相关概念定义的基础上,提出了一种识别用户偏好漂移的方法。该方法在两阶段层次聚类架构中引人多级超图分割算法,通过两组实验验证了方法的有效性。本文对方法复杂性和应用机制也进行了探讨。
User preference drift recognition is one of the keys to update user profile and keep the description precision of users＇ preference.With the quick development of mobile commerce,such recognition was paid great attention recently. However,most of researches based on clustering are insufficient for the treatment of item objects where weak N-ary associations exist.In this paper,through the analysis of contextual recommendation,a hypergraph model of contextual items is proposed,and the similarity between a pair of items,a pair of item clusters and user preference drift degree are defined.Based on above related definitions,a method to measure preference drift is constructed which is based on two stages hierarchical clustering framework and in combination with Multilevel k-way Hypergraph Partitioning arithmetic.Finally the time complexity and application mechanism of the method are discussed,the usefulness of the method is also verified by two groups of experiments.
出处
《情报学报》
CSSCI
北大核心
2011年第8期802-811,共10页
Journal of the China Society for Scientific and Technical Information
基金
国家自然科学基金项目“微内容生产加工模式及其支持平台的研究”（71071066）
国家自然科学基金重点项目“移动商务的基础理论与技术方法研究”（70731001）
关键词
超图模式
情境化推荐
偏好漂移
hypergraph
contextual recommendation
preference drift
