  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.
