KATRec: Knowledge Aware aTtentive Sequential Recommendations release_do5irumjsnegzair4fdn25aggq

by Mehrnaz Amjadi, Seyed Danial Mohseni Taheri, Theja Tulabandhula

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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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Type  article
Stage   submitted
Date   2021-07-06
Version   v3
Language   en ?
arXiv  2012.03323v3
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