考虑多层次潜在特征的个性化推荐模型

Personalized recommendation model with multi-level latent features

个性化推荐已成为解决信息过载的最有效手段之一,也是海量数据挖掘研究领域的热点技术。然而传统推荐算法往往只使用用户对物品的评分信息,而缺少对用户与物品潜在特征的综合考虑。基于因子分解机、宽神经网络、交叉网络和深度神经网络的融合,提出一种新的考虑多层次潜在特征的模型,可以提取用户与物品的浅层潜在特征、低阶非线性潜在特征、线性交叉潜在特征以及高阶非线性潜在特征。在4个常用的数据集上的实验结果表明,考虑用户与物品多层次潜在特征可以有效提高个性化推荐的预测精度。最后,研究了嵌入层维度以及神经元数量等因素对新模型预测性能的影响。

Personalized recommendation has become one of the most effective means to solve information overload,and it is also a hot technology in the research field of massive data mining.However,traditional recommendation algorithms often only use the user’s rating information on the item,and lack a comprehensive consideration of the potential charac-teristics of the user and the item.The factorization machine,wide neural network,crossover network and deep neural network were combined to extract the shallow latent features,low-order nonlinear latent features,linear cross latent fea-tures,and high-order nonlinear latent features of users and items.Thus,a new deep learning personalized recommenda-tion model with multilevel latent features was established.The experimental results on four commonly used data sets show that considering the multi-level potential features of users and items can effectively improve the prediction accura-cy of personalized recommendations.Finally,the influence of factors such as the dimensions of the embedding layer and the number of neurons on the prediction performance of the new model was studied.