Wu Di, Shang Mingsheng, Luo Xin, Wang Zidong
IEEE Trans Neural Netw Learn Syst. 2022 Oct;33(10):5775-5788. doi: 10.1109/TNNLS.2021.3071392. Epub 2022 Oct 5.
A recommender system (RS) is highly efficient in filtering people's desired information from high-dimensional and sparse (HiDS) data. To date, a latent factor (LF)-based approach becomes highly popular when implementing a RS. However, current LF models mostly adopt single distance-oriented Loss like an L norm-oriented one, which ignores target data's characteristics described by other metrics like an L norm-oriented one. To investigate this issue, this article proposes an L -and- L -norm-oriented LF ( [Formula: see text]) model. It adopts twofold ideas: 1) aggregating L norm's robustness and L norm's stability to form its Loss and 2) adaptively adjusting weights of L and L norms in its Loss. By doing so, it achieves fine aggregation effects with L norm-oriented Loss 's robustness and L norm-oriented Loss 's stability to precisely describe HiDS data with outliers. Experimental results on nine HiDS datasets generated by real systems show that an [Formula: see text] model significantly outperforms state-of-the-art models in prediction accuracy for missing data of an HiDS dataset. Its computational efficiency is also comparable with the most efficient LF models. Hence, it has good potential for addressing HiDS data from real applications.
推荐系统（RS）在从高维稀疏（HiDS）数据中过滤人们所需信息方面效率很高。迄今为止，基于潜在因子（LF）的方法在实现推荐系统时变得非常流行。然而，当前的LF模型大多采用单一的面向距离的损失，如基于L范数的损失，而忽略了其他度量（如基于L范数的度量）所描述的目标数据的特征。为了研究这个问题，本文提出了一种基于L范数和L范数的LF（[公式：见正文]）模型。它采用了两个思路：1）聚合L范数的鲁棒性和L范数的稳定性来形成其损失；2）在其损失中自适应调整L范数和L范数的权重。通过这样做，它利用基于L范数的损失的鲁棒性和基于L范数的损失的稳定性实现了良好的聚合效果，以精确描述带有异常值的HiDS数据。在由实际系统生成的九个HiDS数据集上的实验结果表明，[公式：见正文]模型在HiDS数据集缺失数据的预测准确性方面显著优于现有模型。其计算效率也与最有效的LF模型相当。因此，它在处理来自实际应用的HiDS数据方面具有良好的潜力。
