基于情景感知的用户兴趣推荐模型

摘要/Abstract

摘要： 随着电子商务和互联网的发展与普及,面向用户的个性化推荐越来越被重视,传统的用户兴趣模型只考虑到用户本身对项目的行为,忽略了用户当时所处情景。因此文中提出了基于情景感知的用户兴趣模型,将用户的浏览行为与情景因素相结合,从两个方面深度挖掘了用户对项目的兴趣,明确了用户对项目的关注度,从而准确地为用户进行聚类,并根据用户聚类的结果对目标用户进行推荐。实验结果表明,该推荐模型的准确率高于其他传统推荐算法的准确率,本模型能更好地挖掘用户兴趣,适应用户的兴趣变化,并且能够更好地解决用户面临的众多信息无从挑选的问题,提高了用户的满意度。因此,需要从多个角度挖掘用户隐藏的信息,能够更好地为用户提供个性化的推荐。

关键词: 个性化推荐, 情景感知, 用户聚类, 用户兴趣

Abstract: With the development and popularization of electronic commerce and Internet,the user-oriented personalized recommendation is getting more and more attention,the traditional user interest models only consider theuser’s own behavior towards the project,while ignoring the user’s scene at that time.Aiming at this problem,this paper proposed a user interest model based on scene perception,combined the user’s browsing behavior with the situational factors,deeply mined user interest in the project from two aspects,cleared user awareness of the project,thus accurate clustered for users,and target users based on user clustering results were recommended.Experimental results show that the accuracy of this model recommendation is higher than that of other traditional recommendation algorithms.It can better mine users’ interests,adapt to the changes of users’ interests,better solve the problem that users have no way to choose a lot of information,and improve users’ satisfaction.Therefore,it is necessary to excavate the hidden information of users from multiple perspectives so as to better provide personalized recommendations for users.

Key words: Context awareness, Personalized recommendation, User clustering, User interest

中图分类号:

TP301

李建军, 侯跃, 杨玉. 基于情景感知的用户兴趣推荐模型[J]. 计算机科学, 2019, 46(6A): 502-506. https://doi.org/

LI Jian-jun, HOU Yue, YANG Yu. User Interest Recommendation Model Based on Context Awareness[J]. Computer Science, 2019, 46(6A): 502-506. https://doi.org/

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