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

Abstract: In the application of social item recommendation, in addition to considering the social relationship between users, mining the implicit relationship features between items also plays a crucial role in enhancing the representation learning ability of users and items. However, existing social item recommendation methods constructed their item graphs using a static method, which was difficult to accurately capture the latent relationship between items, and the subsequent graph fusion process lacked deep interaction, which may limit the model's ability to understand the complex and multi-level relationship between the features of multiple graphs. To this end, a multi-task social item recommendation based on dynamic adaptive generation of item graph (IMTSR) is proposed. Firstly, in the joint training process based on multi-task learning, the item graph dynamic generation module adaptively adjusted the item graph structure by combining the feedback information of downstream recommendation tasks. Secondly, the social item recommendation module used a deep multi-graph feature fusion mechanism to iteratively propagate the feature representation of users and items among input graphs, so as to capture the complex correlation between multiple graphs more deeply. Finally, comparison experiments with six strong baselines on two public datasets show that IMTSR improved by at least 3% on each evaluation metric compared to the sub-optimal recommendation results. The robustness of IMTSR has been fully verified in the evaluation experiment for cold user and cold item recommendation, which indicates that IMTSR can alleviate the recommendation problem of cold user and cold item with sparse interactions to some extent.

Key words: social network, graph neural network, multi-task learning, graph representation learning, social item recommendation