MB-ATMK:融合属性权重和时序元知识的多行为序列推荐模型  

MB-ATMK:Multi-behavior Sequential Recommendation Integrating Attribute Weights and Temporal Meta-knowledge

作　　者：陈毓哲 曹琼[1] 黄贤英[1] 邹世豪 CHEN Yuzhe;CAO Qiong;HUANG Xianying;ZOU Shihao(College of Computer Science and Engineering,Chongqing University of Technology,Chongqing 400054,China)

机构地区：[1]重庆理工大学计算机科学与工程学院,重庆400054

出　　处：《计算机科学》2024年第S02期607-615,共9页Computer Science

基　　金：国家自然科学基金(62141201);重庆市自然科学基金(CSTB2022NSCQ-MSX1672)。

摘　　要：序列推荐根据用户和项目的交互序列预测用户未来的偏好,然而现有的方法忽略了在现实场景中用户的多行为交互(如浏览、收藏、加入购物车)。其次,用户的偏好有着时序依赖性,同时也受到属性信息的影响。最后,在多行为序列推荐场景中用户的多行为交互存在复杂依赖关系。因此我们提出了一种融合属性权重和时序元知识的多行为序列推荐模型(MB-ATMK)。首先加入用户的多行为交互数据,并基于用户交互的时间戳设计了时序感知编码模块,通过时序感知注意力捕获了用户的动态偏好。其次引入了用户端和项目端丰富的属性信息,设计了属性权重增强的元知识图神经网络。使用元知识提炼了用户的多偏好模式,并基于图神经网络设计了属性权重注意力机制,增强了模型对用户细粒度偏好的捕获。最后提出了包含多行为权重生成模块和偏好迁移网络的元知识预测层,通过生成定制的元知识捕获了用户的跨行为依赖。在两个数据集上进行的大量实验验证了所提模型的有效性和优越性。Sequential recommendation predicts users’future preferences based on the sequence of interactions between users and items.However,existing methods often overlook the multi-behavior interactions(such as page view,favorite,add to cart)in real-world scenarios.Additionally,users’preferences not only depend on temporal sequences but are also influenced by attribute information.Lastly,in the scenario of multi-behavior sequence recommendation,users’multi-behavior interactions exhibit complex dependencies.Therefore,this paper proposes a multi-behavior sequence recommendation model with attribute weights and temporal meta-knowledge(MB-ATMK).Firstly,we incorporate users’multi-behavior interaction data and design a temporal-aware encoding module based on the timestamps of user interactions to capture users’dynamic preferences through temporal-aware attention.Secondly,we introduce rich attribute information on both the user and item sides and design an attribute-weighted meta-knowledge graph neural network.Using meta-knowledge,we refine users’multi-preference patterns and design an attribute-weighted attention mechanism based on graph neural networks to enhance the model’s capture of users’fine-grained preferences.Finally,we propose a meta-knowledge prediction layer that includes a multi-behavior weight generation module and a preference transfer network,capturing users’cross-behavior dependencies through generated customized meta-knowledge.Extensive experiments on two datasets validate the effectiveness and superiority of the proposed model.

关 键 词：序列推荐 多行为推荐 图神经网络 注意力机制 属性信息 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]