基于图共现增强多层感知机的会话推荐

Session-based recommendation based on graph co-occurrence enhanced multi-layer perceptron

针对多层感知机(MLP)架构无法捕获会话序列上下文中的共现关系的问题,提出了一种基于图共现增强MLP的会话推荐模型GCE-MLP。首先,利用MLP架构捕获会话序列的顺序依赖关系,同时通过共现关系学习层获得序列上下文中的共现关系,并通过信息融合模块得到会话表示;其次,设计了特定的特征选择层,旨在扩大不同关系学习层输入特征的差异性;最后,通过噪声对比任务最大化两种关系表征之间的互信息,进一步增强对会话兴趣的表征学习。在多个真实数据集上的实验结果表明GCE-MLP的推荐性能优于目前主流的模型,验证了该模型的有效性。与最优的MLP架构模型FMLP-Rec(Filter-enhanced MLP for Recommendation)相比,在Diginetica数据集上,P@20最高达到了54.08%,MRR@20最高达到了18.87%,分别提升了2.14和1.43个百分点;在Yoochoose数据集上,P@20最高达到了71.77%,MRR@20最高达到了31.78%,分别提升了0.48和1.77个百分点。

Aiming at the problem that the Multi-Layer Perceptron(MLP)architecture can not capture the co-occurrence relationship in the context of session sequence,a session-based recommendation model based on Graph Co-occurrence Enhanced MLP(GCE-MLP)was proposed.Firstly,the sequential dependency of the session sequence was captured by the MLP architecture,and at the same time,the co-occurrence relationship in the sequence context was obtained through the cooccurrence relationship learning layer,and the session representation was obtained through the information fusion module.Secondly,a specific feature selection layer was designed to amplify the diversity of input features of different relation learning layers.Finally,the representation learning of sessional interest was further enhanced by maximizing the mutual information between two relational representations via a noise contrastive task.Experimental results on multiple real datasets show that the recommendation performance of the GCE-MLP is better than those of the current mainstream models,which verifies the effectiveness of GCE-MLP.Compared with the optimal MLP architecture model FMLP-Rec(Filter-enhanced MLP for Recommendation),GCE-MLP achieves the P@20 of 54.08%and the MRR@20 of 18.87%for Diginetica dataset,which are respectively increased by about 2.14 and 1.43 percentage points;GCE-MLP achieves the P@20 of 71.77%and the MRR@20 of 31.78%for Yoochoose dataset,which are respectively increased by about 0.48 and 1.77 percentage points.