User Preference Excitation Network for Sequential Recommendation

Predicting users' preferences based on historical interactions plays a critical role in recommender systems. Conventionally, users' recent sequence behaviors are fed into a model to predict the next item that a user is most interested in. However, most of the existing methods simply utilize the user's recent behavior sequences to model short-term preferences. This is problematic, because users may...