改进填补法和多权重相似度相结合的推荐算法

针对传统推荐算法中存在的数据稀疏性问题，国内外许多研究人员都提出了相应的推荐算法。然而，在个性化推荐方面，其中大多数并没有取得很好的推荐效果。因此，提出改进填补法和多权重相似度相结合的推荐算法，该算法首先采用改进填补法填充缺失值并对数据降维，接着分别计算社交网络用户信任度和改进的二部图用户关联度，最后采用多权重因子将这两者相似度进行结合。基于此，该算法根据相似度高低获取邻居用户并对目标用户进行商品推荐。实验结果表明，在数据稀疏性以及个性化推荐情况下，该算法的平均绝对误差（MAE）优于其他推荐方法。

Recommendation algorithm based on filling method and multi-weight similarity

In order to solve the problem of data sparsity in traditional recommendation algorithms, many researchers at home and abroad have proposed corresponding recommendation algorithms. However, most of these algorithms have not achieved good recommendation results in personalized recommendation. Therefore, this paper proposed a recommendation algorithm based on improved filling method and multi-weight similarity. Firstly, the algorithm filled missing values and reduced data dimension by improved filling method, then calculated user trust degree and user association degree of bipartite graph respectively, and finally used multi-weight factor to combine the two similarities. Based on this, this algorithm obtained neighbor users according to similarity and made recommendation to target users. The experimental results show that the MAE of proposed algorithm is superior to other recommendation methods in the case of sparse data and personalized recommendation.