【摘要】 随着Web2.0的发展,UGC标签系统受到越来越多的关注,标签既能反映用户的兴趣又能描述物品的本身特征。现有的标签推荐算法没有考虑用户的连续行为所产生的影响,而传统的基于马尔可夫链(Markov Chain)的推荐算法虽然侧重于研究用户的连续行为来产生推荐,但它是直接作用于用户与物品的二维关系,并不适用于基于UGC的标签推荐。因此,通过结合马尔可夫链和协同过滤的思想,提出了一种基于标签的个性化推荐算法。该算法将〈用户-标签-物品〉的三维关系拆分为〈用户-标签〉和〈标签-物品〉两个二维关系。首先通过马尔可夫链模型计算用户对标签的兴趣度,再通过推荐标签集来匹配与其相对应的物品。为了提高推荐的精准率,该算法利用标签之间的影响,并基于匹配物品中所含标签间存在的关联关系对物品进行满意度建模,该模型是一种概率模型。在计算用户-标签和用户-物品之间的兴趣度和满意度时使用了协同过滤的思想来补全稀疏值。在公开的数据集中,与现有算法相比,该算法在精准率、召回率上均有明显提高。更多还原

【Abstract】 With the development of Web2.0,UGC tag system is receiving more and more attention.Tag can not only reflect users’ interests,but also it can describe the innate character of item.Available tag recommendation algorithm does not considerae the influence of continuous behaviors of users.Although traditional recommendation algorithm based on Markov Chain produces recommendation through the emphasis on the research of continuous behaviors of users,it can not be appliedy to the tag recommendation of UCG due to its direct function on the two-dimensional relationships between user and item.Therefore,according to the thoughts of Markov Chain and Collaborative Filtering,an individual recommendation algorithm based on the tag could be applied.The algorithm splits three-dimensional relationships of 〈user-tag-item〉 into two two-dimensional relationships of 〈user-tag〉 and 〈tag-user〉.Firstly,the interest degree is calculated through the application of Markov Chain.Then correspondent item matched through the recommendation of tags.To raise the accuracy rating of recommendation,modeling of satisfaction is established by this tag according to the influence of tags and associated relationships among tags of items.This model is a kind of probabilistic model.At the same time of calculating the interest degree and satisfaction degree of user-tag and user-item,the thought of Collaborative Filtering is also used to complement sparse data.Compared with available algorithm,this algorithm is improved a lot in the aspects of precision and recall rate on the open data set.更多还原