基于多空间属性信息融合的序列推荐

Sequential Recommendation Based on Multi-space Attribute Information Fusion

序列推荐旨在从用户的历史行为中建模用户不断变化的兴趣,从而做出与用户兴趣相关的推荐。近年来,物品属性信息被证明可以提升序列推荐的性能,很多工作基于属性信息融合去提升序列推荐的性能,都取得了成效但仍存在一定的不足。首先,它们没有显式地建模出用户对物品属性的偏好或者只建模了一个属性偏好向量,无法充分表达用户的偏好。其次,它们的物品属性信息融合过程未考虑用户个性化信息的影响。因此,针对上述不足,提出了基于多空间属性信息融合的序列推荐(MAIF-SR)。文中提出了多空间属性信息融合框架,在不同的属性空间下融合属性序列并建模出用户对不同属性的偏好,用多维兴趣充分表达用户的偏好;设计了个性化属性注意力机制,在融合信息的过程中引入用户个性化信息,增强融合信息的个性化效果。在两个公开数据集以及一个工业私有数据集上进行实验,结果表明,MAIF-SR优于用于对比的基于属性信息融合的序列推荐。

The goal of sequential recommendation is to model users’dynamic interests from their historical behaviors,and hence to make recommendations related to the users’interests.Recently,attribute information has been demonstrated to improve the performance of sequential recommendation.Many efforts have been made to improve the performance of sequential recommendation based on attribute information fusion,and have achieved success,but there are still some deficiencies.First,they do not explicitly model user preferences for attribute information or only model one attribute information preference vector,which cannot fully express user preferences.Second,the fusion process of attribute information in existing works does not consider the in-fluence of user personalized information.Aiming at the above-mentioned deficiencies,this paper proposes sequential recommendation based on multi-space attribute information fusion(MAIF-SR),and proposes a multi-space attribute information fusion framework,fuse attribute information sequence in different attri-bute information spaces and model user preferences for different attribute information,fully expressing user preferences using multi-dimensional interests.A personalized attribute attention mechanism is designed to introduce user personalized information during the fusion process,enhance the personalized effect of the fusion information.Experimental results on two public data sets and one industrial private data set show that MAIF-SR is superior to other comparative sequential recommendation models based on attribute information fusion.