融合全局和近邻协同信息的会话推荐算法

现有基于会话的推荐算法主要通过挖掘单个目标会话的项目转换关系进行推荐，对来自其他不同会话中项目之间的复杂转换信息考虑较少。为此，提出一种融合全局和近邻协同信息的会话推荐算法SFGN-GNN,同时考虑来自全局与近邻会话的协同信息，以充分挖掘用户偏好。通过学习会话表示来表达用户偏好，先按目标会话与近邻会话的成对项目转移关系构建近邻图，依据所有会话中的成对项目转移关系构建全局图，再利用图神经网络获取目标会话节点近邻级和全局级的项目表示，采用融合门融合得到会话级项目表示，并在其中嵌入项目在目标会话中的位置信息和时间信息，然后通过软注意力机制得到最终的会话表示，最后经过softmax函数预测下一个可能交互的项目。在两个数据集上的实验验证了SFGN-GNN算法有效性。

Session-based recommendation algorithm fusing global and neighbor collaboration information

Existing session-based recommendation algorithms mainly recommend by mining the item conversion relationship of a single target session, and take less account of the complex conversion information between items from other different sessions. This paper proposed a session-based recommendation algorithm fusing global and neighbor collaboration information(SFGN-GNN), which simultaneously considered the collaboration information from neighbor and global sessions to fully exploit user preference. The algorithm expressed user preference by learning session representation. Firstly, it built the neighbor graph according to the pairwise item transfer relationship between the target session and neighbor session, and built the global graph according to the pairwise item transfer relationship in all sessions. Then, it used the graph neural network to obtain the neighbor-level and global-level item representations of the target session node. Next, it used the fusion gate to obtain the session-level item representation, and embeded the position information and time information of the item in the target session. Afterward, it obtained the final session representation by using the soft attention mechanism. Finally, it predicted the next possible interaction item by softmax function. Experiments on two datasets demonstrate the effectiveness of SFGN-GNN algorithm.