摘要
随着网络信息的爆炸式增长,推荐系统在缓解信息过载和信息迷航问题方面发挥着关键作用.如何更好地利用海量的网络信息挖掘用户的偏好和项目的特征成为当前研究的热点.针对这一热点,本文设计了一种深度混合模型从而更充分的提取文本上下文信息特征辅助推荐,提出的基于循环卷积神经网络的上下文感知协同过滤推荐模型通过利用循环卷积神经网络挖掘项目描述文本上下文信息中的特征,再结合概率矩阵分解实现评分预测.此外,探究利用多头注意力机制重点关注文本上下文信息中的多项重要信息.模型在两个公开数据集ML-100k和ML-10m上进行了实验,实验结果表明,本研究所提出的模型在RMSE和MAE评价指标上相较于广泛使用的基线模型有明显改进,其中RMSE指标在ML-100k数据集上的有效性比aSDAE高出5.42%.
With the explosive growth of network information,recommendation systems play a key role in alleviating the problems of information overload and information wandering.How to better utilize massive network information to mine user preferences and item characteristics has become a hot issue in current academic research.Targeting at this hot issue,the author put forward a context-aware collaborative filtering recommendation model based on circular convolutional neural network.The model uses recurrent convolutional neural network to mine item features in text auxiliary information,and combines probability matrix decomposition model to realize rating prediction.At the same time,it explores using multi-head attention mechanism to focus on multiple important information in auxiliary information.The model was tested on two publicly available datasets,ML-100k and ML-10m,and the experimental results showed that the proposed model had significant improvements in RMSE and MAE evaluation metrics compared to the widely used baseline model.Among them,the RMSE metric was 5.42%more effective on the ML-100k dataset than aSDAE which was the currently best.
作者
王凤姣
王一晴
段超
WANG Feng-jiao;WANG Yi-qing;DUAN Chao(School of Digital Information Engineering,Minjiang Teachers College,Fuzhou Fujian 350001,China;Fujian University Applied Engineering Centre for Internet of Things,Fuzhou Fujian 350000,China;Zhejiang Provincial Key Laboratory of Intelligent Education Technology and Application,Zhejiang Normal University,Jinhua Zhejiang 321004,China)
出处
《淮阴师范学院学报（自然科学版）》
CAS
2024年第1期27-34,共8页
Journal of Huaiyin Teachers College;Natural Science Edition
基金
国家自然科学基金项目(62207027,62177024)
浙江省智能教育技术与应用重点实验室项目(jykf22029)
浙江省教育科学规划课题(2023SCG369)
教育部产学合作协同育人项目(220906424035704)。
关键词
推荐系统
循环神经网络
卷积神经网络
注意力机制
概率矩阵分解
recommender system
recurrent neural network
convolutional neural network
attention mechanism
probabilistic matrix factorization
