基于会话的独立邻域矩阵偏好交互推荐

Session-based Recommendation with Preference Interaction from Separate Adjacent Matrix

通过合并输入和输出邻域矩阵可以使一些工作生成全局和局部偏好,并直接对这2个偏好建模来构建会话表示,从而实现改进。然而,一个会话的输入矩阵和输出矩阵并没有很强的相关性,它们的连接可能会为构建2个偏好引入噪声。其次,全局偏好和局部偏好可以相互促进,且邻域会话的协同信息可能有助于提高推荐性能。因此,一种基于会话的偏好交互推荐被提出,它来自独立的输入邻域矩阵和输出邻域矩阵框架,包括2个并行模块:输入会话表示编码器(ISE)和输出会话表示编码器(OSE)。ISE通过GNN和并行协同注意力机制对具有输入信息的会话表示进行建模。OSE通过GNN和并行协同注意力机制对具有输出信息的会话表示进行建模。最后,引入一种融合门控机制来平衡ISE和OSE产生的会话表示的重要性。结果表明,在Yoochoose和Diginetica数据集上,提出的模型明显优于其他先进的方法。

Improvements can be achieved by incorporating incoming and outgoing adjacent matrices to generate global and local preferences,and directly model the two preferences to build session representation.However,the incoming matrix and outgoing matrix of a session have no strong relevance,and their concatenation may introduce noise for building two preferences.Secondly,the global and local preferences can benefit from each other,and collaborative information of neighborhood sessions may help to improve recommendation performance.Therefore,a session-based recommendation with preference interaction from separate incoming adjacent matrix and outgoing adjacent matrix framework was proposed,which includes two parallel modules:an incoming session representation encoder(ISE)and an outgoing session representation encoder(OSE).The ISE models session representation with incoming information through GNN and parallel co-attention mechanism.The OSE models session representation with outcome information through GNN and parallel co-attention mechanism.Finally,a fusion gating mechanism is introduced to balance the importance of session representations resulting from ISE and OSE.The experimental results show that proposed model obviously outperforms other state-of-the-art methods on Yoochoose and Diginetica datasets.