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.
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