KATRec: Knowledge Aware aTtentive Sequential Recommendations
This work provides an incremental improvement for e-commerce platforms and content providers seeking to enhance the accuracy of their sequential recommendation systems.
This paper addresses the challenge of modeling short-term and long-term user preferences in sequential recommendation systems. The authors propose KATRec, a knowledge graph-enhanced model that leverages item multi-relations and user dynamic sequences, outperforming state-of-the-art models on three public datasets.
Sequential recommendation systems model dynamic preferences of users based on their historical interactions with platforms. Despite recent progress, modeling short-term and long-term behavior of users in such systems is nontrivial and challenging. To address this, we present a solution enhanced by a knowledge graph called KATRec (Knowledge Aware aTtentive sequential Recommendations). KATRec learns the short and long-term interests of users by modeling their sequence of interacted items and leveraging pre-existing side information through a knowledge graph attention network. Our novel knowledge graph-enhanced sequential recommender contains item multi-relations at the entity-level and users' dynamic sequences at the item-level. KATRec improves item representation learning by considering higher-order connections and incorporating them in user preference representation while recommending the next item. Experiments on three public datasets show that KATRec outperforms state-of-the-art recommendation models and demonstrates the importance of modeling both temporal and side information to achieve high-quality recommendations.