计算机科学 ›› 2020, Vol. 47 ›› Issue (6A): 494-499.doi: 10.11896/JsJkx.190900016
邹海涛, 郑尚, 王琦, 于化龙, 高尚
ZOU Hai-tao, ZHENG Shang, WANG Qi, YU Hua-long and GAO Shang
摘要: 现有的一些算法引入了隐语义模型克服数据稀缺带来的问题,为用户提供更有效的推荐。一般情况下,这些方法通过线性组合若干多项式,引入相应参数平衡各个部分比重,以构造优化函数,最终达到最小评分误差或实现最大的偏好等目的。经典模型通常只考虑用户对某一产品的预测评分和实际评分差异(即,一阶评分距离),忽略了其在不同产品上的预测评分与实际评分之间的差值 (即,二阶评分距离)。因此,高阶评分距离模型同时将两种距离集成到算法之中,并使用随机梯度下降法求解目标函数。可是,上述优化函数中的相关参数往往是手动设置,而且随机梯度下降法求解目标函数的收敛速度较慢,使得该模型缺乏灵活性,也增加了时间消耗。为了提高模型的适应性和效率,文中提出了一种融合归一化函数的自适应高阶评价距离模型,并利用牛顿法求解改进后的高阶评分距离凸优化函数。此方法不仅移除了若干静态参数,而且加快了优化函数的收敛速度。提出的模型具有坚实的理论支持,经过3个实际数据集的实验结果表明,此模型具有较好的预测精度和运行效率。
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