融合上下文信息的个性化序列推荐深度学习模型

Deep Learning Model Based on Contextualized Personalized Sequence Recommendation

针对现实购物场景中存在的用户偏好多样性且兴趣动态变化的问题,本文提出一种融合上下文信息的序列推荐模型(DeepSeq),通过嵌入用户提供的反馈信息深入挖掘用户的长短期潜在兴趣,有效解决了传统推荐系统无法模拟用户兴趣进化的问题.该文以真实的电商网站数据为背景,首先,利用历史行为数据和项目辅助信息融合构造长短期会话序列并融合上下文信息,提出兴趣衰减因子反应用户偏好变化.其次,基于文本卷积模型(TextCNN)训练得出序列向量表示,并通过多头注意力机制抽取用户项目序列潜在向量;最后,将用户交叉辅助信息和潜在行为特征组合向量输入到多层感知机,建立基于序列的推荐模型.实验结果表明,在行为序列中融合兴趣衰减因子和项目辅助信息,均有效提高了模型的准确率.此外,DeepSeq相对于传统的推荐模型在评价指标RMSE上至少降低了0.21%,并且在GAUC评价指标上提升值均超过了0.59%.

Aiming at the user preference diversity and interests dynamics in real shopping scenarios,this paper proposes a personalized sequence recommendation deep learning model that fusion the contextual information.More effectively explore user′s long-term and short-term interests by embedding feedback information provided by users and effectively solve the problem that the traditional recommendation system can′t simulate the evolution of user interest.This article takes real e-commerce website data as a background.Firstly,it uses historical behavior data and item auxiliary information to construct long-and short-term session sequences and fuse contextual information,and proposes interest attenuation factors to reflect changes in user preferences.Secondly,based on the TextCNN model training to obtain sequence vector representation and extract the user item sequence latent vector through the multi-head attention mechanism;Finally,the combination vector of user′s cross information and potential behavior characteristics is fed to the multilayer perceptron to establish a sequence-based recommended model.Experimental on two real datasets and a public datasets,show that adding interest decay factor and project auxiliary information to the behavior sequence improves the model performance.In addition,the prediction model based on this paper has improved the evaluation indicators RMSE and GAUC compared with the traditional recommendation algorithm.