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Found 7 papers, 0 papers with code
CSPM: A Contrastive Spatiotemporal Preference Model for CTR Prediction in On-Demand Food Delivery Services
Click-through rate (CTR) prediction is a crucial task in the context of an online on-demand food delivery (OFD) platform for precisely estimating the probability of a user clicking on food items.
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.
Mobile Supply: The Last Piece of Jigsaw of Recommender System
In order to address the problem of pagination trigger mechanism, we propose a completely new module in the pipeline of recommender system named Mobile Supply.
ESMC: Entire Space Multi-Task Model for Post-Click Conversion Rate via Parameter Constraint
We propose a novel Entire Space Multi-Task Model for Post-Click Conversion Rate via Parameter Constraint (ESMC) and two alternatives: Entire Space Multi-Task Model with Siamese Network (ESMS) and Entire Space Multi-Task Model in Global Domain (ESMG) to address the PSC issue.
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.
