基于标签的评分信息熵推荐算法

由于标签是由用户根据自己的理解和喜好随意进行标注的因此存在大量的噪声标签，导致基于标签的推荐系统准确率不高.针对这种情况，提出了结合评分信息熵的标签推荐算法.算法通过判断用户在标注标签的评分稳定程度来确定该标签对于用户的重要性从而过滤掉噪声标签将重要标签赋予较高权重，并构建用户的兴趣模型，最后应用到协同过滤算法中产生推荐.该算法能有效地利用评分权重并结合信息熵来增强推荐准确率，与以往的基于标签的推荐算法进行对比，能获得满意的推荐效果.

Label-Based Score Information Entropy Recommendation Algorithm

As the label is marked by the user according to their own understanding and preferences, the expression of the concept is fuzzy and there are a large number of noise tags, resulting in the low efficiency of the traditional label-based recommendation algorithm recommended. casein view of this problem, a tag recommendation algorithm combining the score information entropy is proposed. The algorithm determines the importance of the tag for the user in order to build the user''s interest model for the rating of the label. The algorithm can effectively use the score weight and combine the information entropy to enhance the recommendation accuracy, and compared with the previous label-based recommendation algorithm, it can get a satisfactory recommendation effect.