Papers with hierarchical user interest matching framework

1 papers
HieRec: Hierarchical User Interest Modeling for Personalized News Recommendation (2021.acl-long)

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Challenge: Existing news recommendation methods learn a single user embedding for each user from their previous behaviors to represent their overall interest. Existing methods only learn 'one' embeddable representation vectors to model user interest.
Approach: They propose a news recommendation method with hierarchical user interest modeling that captures user interest in news rather than a single user embedding.
Outcome: The proposed method can better capture multi-grained user interest in news.

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