Long Short-Term Preference Modeling for Continuous-Time Sequential Recommendation

Understand

Modeling the evolution of user preference is essential in recommender systems.

• Recently, dynamic graph-based methods have been studied and achieved SOTA for recommendation, majority of which focus on user's stable long-term preference.
• However, in real-world scenario, user's short-term preference evolves over time dynamically.
• Although there exists sequential methods that attempt to capture it, how to model the evolution of short-term preference with dynamic graph-based methods has not been well-addressed yet.