结合评分习惯加权的稀疏矩阵插值推荐技术

插值估计可缓解推荐系统的稀疏问题，但其效果会影响预测性能。以logistic用户习惯（habit）评分加权改进Jaccard（HabJac）相似度量，并通过K近邻获得插补评分。进而，通过融合正则化奇异值分解（RSVD）技术提出了新的HISVD推荐算法，并获得最终预测。用户的习惯评分被定义为其出现频次最高的评分，并且logistic权值同评分与习惯评分之间的欧氏距离正相关。在四个现实数据集上的实验结果表明：a）HISVD在不同数据集上，最优情况下的参数比较稳定；b）HISVD在MAE和RSME指标上均超过了主流算法。

Sparse matrix interpolation recommendation technology combined with scoring habit weighting

The imputation-based solution can alleviate the sparsity problem of recommendation system. Improved Jaccard similarity based on logistic user habit rating weighting, which is called the HabJac. By combining this metric, the K nearest neighbor(KNN) obtains the imputation value. Furthermore, this paper proposed a new HISVD recommendation algorithm by combining the regularized singular value decomposition(RSVD) technology to predict the unknown ratings. Firstly, the user''s habit rating was the most frequent one. Secondly, logistic weight was positively correlated with Euclidean distance between rating and habit one. The experimental results on four real data sets show that: a) the optimal parameters of HISVD algorithm for different data sets are similar; b) HISVD surpasses the mainstream competitors for the MAE and RSME.