基于内容的加权粒度序列推荐算法

为了提高个性化推荐系统的准确率,提出了一种基于内容的加权粒度序列推荐算法。通过分析项目属性关系将项目粒度化,计算每个粒度的贡献度得到项目特征矩阵。再根据用户行为信息生成用户粒度序列并进行粒度映射,利用Apriori算法提取出用户偏好矩阵。最后将项目特征矩阵和用户偏好矩阵做乘积运算,其结果代入改进的sigmoid函数中进行喜好概率预测,从而完成Top-N项目推荐。实验选取MovieLens数据集,结果表明基于内容的加权粒度序列的推荐算法准确率达到72.27%,高于当前流行的推荐算法;在效率方面,推荐时间少于相同用户数量下的协同过滤推荐算法;综合测度评分为0.393,充分验证了算法的整体性能优于其他推荐算法。

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Based on the output data’s reconstructing the original data, classical auto encoders(BAE、SAE、DAE、CAE) can extract low dimensional features of the input information.Applying these features in image classification may not able to guarantee a good result.In this paper,we use the label information to propose a stacked discriminant auto encoder (SDcAE),which adds the class encoderas the constraint of hidden layer neuron into the training process of the stacked auto encoder. Hence, the features learned by the hidden layer have better discrimination ability. In addition,we propose a class encoding classifier(CEC),which adds the class encoder as the discriminative loss into the Softmax classifier.Due to the decrease of the feature error of the inter class samples, the classifier can achieve better training results, thus improving the final classification accuracy.The experimental results show that the stacked discriminant auto encoder and the class encoding classifier are effective and feasible in image classification.