Recently, relational metric learning methods have been received great
attention in recommendation community, which is inspired by the translation
mechanism in knowledge graph. Different from the knowledge graph where the
entity-to-entity relations are given in advance, historical interactions lack
explicit relations between users and items in recommender systems. Currently,
many researchers have succeeded in constructing the implicit relations to remit
this issue. However, in previous work, the learning process of the induction
function only depends on a single source of data (i.e., user-item interaction)
in a supervised manner, resulting in the co-occurrence relation that is free of
any semantic information. In this paper, to tackle the above problem in
recommender systems, we propose a joint Semantic-Enhanced Relational Metric
Learning (SERML) framework that incorporates the semantic information.
Specifically, the semantic signal is first extracted from the target reviews
containing abundant item features and personalized user preferences. A novel
regression model is then designed via leveraging the extracted semantic signal
to improve the discriminative ability of original relation-based training
process. On four widely-used public datasets, experimental results demonstrate
that SERML produces a competitive performance compared with several
state-of-the-art methods in recommender systems.

通过引入语义信息，我们提出了一种联合语义增强关系度量学习 (SERML) 框架，该框架从目标评论中提取语义信号，并通过该信号改进了原始基于关系的训练过程的区分能力，实验证明 SERML 在推荐系统中表现与几种先进方法相当。