摘要
个性化推荐技术在电子商务系统中得到了广泛的应用。针对现有商品特征算法不能反映出用户对商品特征认识的差异问题,提出了一种用户显意识下的多重态度个性化推荐算法,引入显意识及多重态度的权值,从不同角度去描述消费者心理特征,使推荐结果更符合用户的需求。实验对比结果表明,用户显意识下的多重态度个性化推荐算法能够提高商品特征推荐算法的推荐精度。
Personalized recommendation technology has been widely applied in e-commerce.For the deficiency of the existing algorithms of product features can not reflect the different understanding of the same product between users,this paper presents a personalized recommendation algorithm based on multi-attribute model of attitude of user salient belief,user salient belief and multi-attribute model of attitude-based data weight are proposed.The algorithm describes user＇s psychology from different view,for this reason,the recommendation result is more satisfied user＇s needs.Experimental results show that,the proposed algorithm outperforms the traditional algorithm based on product features algorithm.
出处
《计算机工程与应用》
CSCD
北大核心
2011年第16期144-146,150,共4页
Computer Engineering and Applications
关键词
商品特征
个性化推荐
显意识
多重态度
product features
personalized recommendation
salient belief
multi-attribute model of attitude
