KATRec: Knowledge Aware aTtentive Sequential Recommendations

TL;DR

KATRec is a novel sequential recommendation system that integrates knowledge graphs with attention mechanisms to better model user preferences over time, leveraging side information for improved accuracy.

Contribution

This paper introduces KATRec, a knowledge graph-enhanced model that captures both short-term and long-term user interests using attention networks and higher-order item relations.

Findings

KATRec outperforms existing models on three public datasets.

Modeling both temporal dynamics and side information improves recommendation quality.

Knowledge graph integration enhances item representation learning.

Abstract

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.