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Found 5 papers, 1 papers with code
Multi-Granularity Attention Model for Group Recommendation
Group recommendation provides personalized recommendations to a group of users based on their shared interests, preferences, and characteristics.
Exploring the Spatiotemporal Features of Online Food Recommendation Service
Online Food Recommendation Service (OFRS) has remarkable spatiotemporal characteristics and the advantage of being able to conveniently satisfy users' needs in a timely manner.
BASM: A Bottom-up Adaptive Spatiotemporal Model for Online Food Ordering Service
Therefore, we address this challenge by proposing a Bottom-up Adaptive Spatiotemporal Model(BASM) to adaptively fit the spatiotemporal data distribution, which further improve the fitting capability of the model.
Spatiotemporal-Enhanced Network for Click-Through Rate Prediction in Location-based Services
In Location-Based Services(LBS), user behavior naturally has a strong dependence on the spatiotemporal information, i. e., in different geographical locations and at different times, user click behavior will change significantly.
Deep Interest with Hierarchical Attention Network for Click-Through Rate Prediction
Deep Interest Network (DIN) is a state-of-the-art model which uses attention mechanism to capture user interests from historical behaviors.
