AFGSRec:一种自适应融合全局协同特征的社交推荐模型

AFGSRec:A Social Recommendation Model Based on Adaptive Fusion of Global Collaborative Features

以往的序列推荐方法通常从近期交易记录中捕获用户的消费偏好,忽略了全局交易信息和好友偏好对用户交易行为的影响,导致模型的推荐结果不够准确。针对以上问题,文中提出了一种自适应融合全局协同特征的社交推荐模型AFGSRec。首先,用异质图神经网络建模社交网络中的用户、历史交易信息,以捕获全局协同特征和好友之间的社交影响;接着,设计了一种基于选择机制的门图神经网络,以有效过滤与当前序列无关的节点转换信息,更准确地捕获用户当前偏好;然后,提出了一种自适应的特征融合方法,以动态捕获全局协同特征对用户偏好的影响,提高系统的推荐准确率;最后,将周期动态学习率用于模型训练,以更好地处理鞍点,提升模型的收敛速度。实验结果表明:AFGSRec具有较好的鲁棒性,命中率(HR)和平均倒数排名(MRR)都优于当前领先模型SERec,在Gowalla数据集上,HR@10、HR@20分别提升了1.91%和1.15%,MRR@10、MRR@20分别提升了5.05%和4.83%;在Delicious数据集上,HR@10、HR@20分别提升了2.45%和1.19%,MRR@10、MRR@20分别提升了4.84%和4.32%。

Previous session-based recommendation systems usually capture users’consumption preferences from their recent transaction records,and this method ignores the influence of global transaction information and friends’preferences on users’transaction behavior,resulting in less accurate recommendation results of the model.To solve the problem,this paper proposed a social recommendation model AFGSRec based on an adaptive fusion of global collaborative features.Firstly,a heterogeneous graph neural network was used to model users and their historical transaction information on the social network for capturing global collaborative features and social influence among friends.Secondly,this paper designed a graph neural network based on a selection mechanism that effectively filters out the node transition features irrelevant to the current session and captures user preferences more accurately.Thirdly,an adaptive fusion method was designed to capture the impact of global collaborative features on users’current preferences dynamically and improve the model’s recommendation accuracy.Finally,this paper used a dynamic cyclical learning rate to help the model better handle saddle points during the training process to improve the convergence speed of model AFGSRec.The experimental results show that AFGSRec is robust;both the HR(Hit Rate)and MRR(Mean Reciprocal Rank)of AFGSRec outperform the state-of-art model SERec.On the Gowalla dataset,HR@10 and HR@20 are increased by 1.91%and 1.15%,respectively;MRR@10 and MRR@20 are increased by 5.05%and 4.83%,respectively.On the Delicious dataset,HR@10 and HR@20 are increased by 2.45%and 1.19%,respectively;MRR@10 and MRR@20 are increased by 4.84%and 4.32%,respectively.