School of Computer Science, Guangdong University of Technology, China.
School of Computer Science, Guangdong University of Technology, China.
Neural Netw. 2020 Apr;124:86-94. doi: 10.1016/j.neunet.2020.01.008. Epub 2020 Jan 20.
Real recommender systems usually contain various auxiliary information. Some of the most recent works make meaningful exploration of incorporating auxiliary information into the representation model for competitive recommendation. However, learning user and item representations still faces two challenges: (1) existing works do not well address the problem of integrating multi-type auxiliary information; (2) learning representations for inactive users is still challenging due to the high sparsity of explicit user-item associations. In order to tackle these problems, in this paper, the attributed heterogeneous network and bipartite interaction network are employed to incorporate various auxiliary information and user-item associations. A joint objective function and an efficient algorithm are devised for the representation learning. Experimental results show that the proposed algorithm has significant advantages over the state-of-the-art baselines. What is remarkable is that our proposed method is demonstrated to be especially useful for dealing with low-active users in the system.
真实的推荐系统通常包含各种辅助信息。最近的一些工作对将辅助信息纳入竞争推荐的表示模型进行了有意义的探索。然而，学习用户和项目表示仍然面临两个挑战：（1）现有工作没有很好地解决集成多类型辅助信息的问题；（2）由于显式用户-项目关联的高度稀疏性，学习不活跃用户的表示仍然具有挑战性。为了解决这些问题，在本文中，使用了带属性的异构网络和二分交互网络来合并各种辅助信息和用户-项目关联。设计了一个联合目标函数和一个有效的算法来进行表示学习。实验结果表明，与最先进的基线相比，所提出的算法具有显著的优势。值得注意的是，我们提出的方法被证明对于处理系统中的低活跃用户特别有用。
