融合多源图信息的图神经网络会话推荐算法

Graph neural networks combined with multi-source graph information for session-based recommendation

现有的基于图神经网络的会话推荐算法通过将会话序列构筑为图形结构捕捉项目转换关系,能够有效提高推荐性能.然而多数图神经网络及其改进模型在建模会话时仅考虑会话序列中项目的单次转换关系,忽略了会话中包含的大量有效信息,同时缺乏对项目间隐藏关联性的分析.因此提出融合多源图信息的图神经网络会话推荐算法.将用户重复行为信息,项目内容关联信息纳入到会话图建模过程当中,有效提取项目更深层次的复杂转换关系,并通过线性转换进行聚合.此外采用外部注意力机制辅助获取会话序列项目隐藏关联信息,使得生成的会话向量在推荐过程中更加精确.在真实数据集Yoochoose和Diginetica上进行实验,实验结果表明该模型优于基准模型,特别地,相较于SR-GNN模型在MRR@20指标上提高了12.50%,能更好地预测用户的下一次点击项目.

Existing session-based recommendations with graph neural networks could capture the item’s transition relationship by constructing graph structures from sessions. However, most graph neural networks and their improved models only consider the single transition relationship of items in the session when modeling sessions. As a result, a large amount of effective information is ignored, and the analysis of hidden correlations between items is lacking. Therefore, a session-based recommendation algorithm with graph neural network and multi-source graph information is proposed. In the algorithm, the users’ repeat behavior information and item content related information are incorporated into the session graph modeling process, which effectively extracts the deeper complex transformation relationship of items, and aggregates it through linear transformations. In addition, an external attention mechanism is used to obtain the hidden association information of the session sequence items, making the generated session vectors more accurate. The experiments were performed on the real datasets: Yoochoose and Diginetica, and the results showed that the model outperformed the benchmark model. In particularly, it outperforms the state-of-the-art benchmark model GC-SAN, on average by 12.50% in terms of the MRR@20 evaluation metric, and can better predict user’s next click items.