基于会话的推荐任务是基于匿名会话来预测用户的下一步操作。该领域现有的模型都是基于当前会话信息来完成用户行为预测，却都忽略会话与会话间存在的相关过渡信息。为此提出一种基于图神经网络捕捉高阶信息推荐算法。首先通过聚合本地邻居与高阶邻居传递信息学习项目的嵌入向量，其次利用注意力机制，针对当前会话进行匿名用户的长短期兴趣挖掘，最终生成预测评分。在真实数据集Yoochoose与Diginetica上的实验结果表明:与当前基于会话的推荐算法相比，所提算法具有更好的推荐性能。

The session-based recommendation task is about predict the user's next action based on an anonymous session. Existing approaches all make prediction by analyzing the current session, ignoring relevant transitions between each sessions the user historically made. Therefore, a recommendation algorithm based on graph neural network to capture higher order information is proposed. Firstly, learning the embedding vector of the item by aggregating information from local neighbors as well as higher order neighbors. Secondly,capturing the short-term and long-term interest with attention mechanism. Finally generate ranking score for predition. Experimental results on real data sets Yoochoose and Diginetica show that the proposed algorithm significantly improves the performance of the recommendation system compared to exisiting session-based recommendation algorithm.

TP301.6

国家自然科学基金资助项目(61902042)；重庆市自然科学基金资助项目(cstc2020jcyj-msxmX0652)