Abstract:

Besides considering the social relationships between users， mining the implicit relationship features between items also plays a crucial role in enhancing the representation learning ability of users and items. The static item graph construction process in current social item recommendation systems is difficult to capture the latent relationships between items accurately， and the subsequent graph fusion process lacks deep interaction， thereby limiting the related models’ ability to understand complex and multi-level relationships among multi-graph features. To this end， a Multi-Task social item recommendation method based on Dynamic Adaptive Generation of item graph （MTDAG） was proposed. Firstly， in joint training process based on Multi-Task Learning （MTL）， the item graph dynamic generation module was used to adjust item graph structure adaptively by combining feedback information from downstream recommendation tasks. Secondly， the social item recommendation module was used to propagate the feature representations of users and items among input graphs iteratively through a deep multi-graph feature fusion method. Finally， extensive experiments on two public datasets， Yelp and Ciao， of comparing MTDAG and six baseline methods such as ECGN （Efficient Complementary Graph convolutional Network） and MGL （Meta Graph Learning framework） were carried out. Experimental results show that MTDAG improves all the Hit Rate （HR）， Recall， and Normalized Discounted Cumulative Gain （NDCG） by at least 3%， and the robustness of MTDAG is fully verified in evaluation experiments for cold user and cold item recommendation， indicating that MTDAG can solve the recommendation problem of cold user and cold item with sparse interaction to some extent.

Key words: social network, Graph Neural Network (GNN), Multi-Task Learning (MTL), graph representation learning, social item recommendation

摘要：

除了考虑用户间的社交关系，挖掘项间隐含的关系特征对提升用户和项的表示学习能力同样具有至关重要的作用。当前社交项推荐中静态项图构建过程难以准确抓取项间的潜在关系，且后续的图融合过程缺乏深度交互，这限制了相关模型对多图特征间复杂且多层次关系的理解能力。因此，提出一种基于项图动态适应性生成的多任务社交项推荐方法（MTDAG）。首先，在基于多任务学习（MTL）的联合训练中，使用项图动态生成模块结合下游推荐任务的反馈信息适应性地调整项图结构；其次，使用社交项推荐模块通过深层次的多图特征融合方法在各输入图之间迭代地传播用户和项的特征表示；最后，在Yelp和Ciao两个公共数据集上把MTDAG与ECGN （Efficient Complementary Graph convolutional Network）和MGL （Meta Graph Learning framework）等6种基线方法比较。实验结果表明，MTDAG在命中率（HR）、召回率（Recall）和归一化折损累计增益（NDCG）上均至少提高了3%且MTDAG的鲁棒性在针对冷用户和冷项推荐的评估实验中得到了充分验证，实验结果表明，MTDAG可以在一定程度上解决交互稀疏的冷用户和冷项的推荐问题。

关键词: 社交网络, 图神经网络, 多任务学习, 图表示学习, 社交项推荐