【摘要】 协同过滤是现行推荐系统中应用最广泛也是最成功的推荐技术之一,然而传统的协同过滤推荐算法存在着邻居选取片面性和推荐精度低的问题。针对上述问题,提出了一种基于重叠度和双重属性的协同过滤推荐算法。首先基于相似度和重叠度的共同计算结果选取推荐对象集;然后提出了双重属性的概念,分别计算推荐用户的信任度和目标项目的受欢迎度;最后兼顾两个群体,根据用户和项目两方面的评分信息完成对目标用户的推荐。实验结果证明该算法较传统的协同过滤推荐算法在邻居选取和推荐质量方面均有显著的提高。更多还原

【Abstract】 Recently,collaborative filtering is one of the most widely used and successful recommendation technology in the recommender system.However,the traditional collaborative filtering recommendation algorithm has the disadvantages of the one-sidedness for selecting neighbors and lower recommendation precision.In order to solve the problems,this paper proposed a collaborative filtering recommendation algorithm based on overlap degrees and double attributes.Firstly,we used the aggregated results of similarity and overlap degrees to select the recommended set of objects.Then,we proposed the concept of double attributes,and calculated the reliability of the target user and the popularity of the target item respectively.At last,taking into account the two groups,we used both user and item rating information to generate recommendation for the target user.Experimental results show the proposed algorithm is improved significantly in terms of neighbor selection and recommendation quality compared to traditional collaborative filtering recommendation algorithm.更多还原