基于网络结构的推荐算法得到了研究者越来越多的关注,以往的基于二部图网络结构的推荐算法只是判断用户是否选择过项目,不区分用户对项目评分的高低.这些算法倾向于推荐流行商品,没有考虑项目度和权值的影响.针对这些问题,在区分高低分的情况下提出了改进的基于加权网络结构的推荐算法.算法在计算用户间的相似性系数时,引入项目度与项目的权值之和的比值θ,以提高推荐多样性.实验结果表明,改进后的算法能够提高推荐准确性和多样性,并且降低了推荐项目的流行性.%In recent years, the recommendation algorithm based on networks has been attracting more and more researchers' attention. However, these recommendation algorithms based on bipartite networks are only to judge whether the user has selected the objects instead of distinguishing the preferences of the user about the object. And these algorithms tend to recommend popular objects, without considering the influence of object degree and the weights of the object. To solve these problems , this paper proposed an improved recommendation algorithm based on weighted networks, which distinguished the level of rating that a user voted an object. At the same time, the ratio 8 of the object degree and the sum of weights of the object were embedded into the similarity index between users to improve the recommendation diversity. Experimental results show that the improved algorithm can improve recommendation accuracy and diversity, while reducing the epidemic of the recommended objects.
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