Semantic-Enhanced Relational Metric Learning for Recommender Systems
This work addresses the issue of semantic-free co-occurrence relations in recommender systems for users and developers, but it is incremental as it builds on existing relational metric learning methods.
The paper tackled the problem of relational metric learning in recommender systems lacking semantic information by proposing a joint Semantic-Enhanced Relational Metric Learning (SERML) framework that incorporates semantic signals from reviews, resulting in competitive performance on four public datasets.
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.