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Found 6 papers, 4 papers with code
RESUS: Warm-Up Cold Users via Meta-Learning Residual User Preferences in CTR Prediction
Specifically, we employ a shared predictor to infer basis user preferences, which acquires global preference knowledge from the interactions of different users.
Differentiable Neural Input Search for Recommender Systems
The dimensions of different feature embeddings are often set to a same value empirically, which limits the predictive performance of latent factor models.
Adaptive Factorization Network: Learning Adaptive-Order Feature Interactions
Various factorization-based methods have been proposed to leverage second-order, or higher-order cross features for boosting the performance of predictive models.
Ranked #2 on
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Incorporating Interpretability into Latent Factor Models via Fast Influence Analysis
The results demonstrate the effectiveness and efficiency of FIA, and the usefulness of the generated explanations for the recommendation results.
Explaining Latent Factor Models for Recommendation with Influence Functions
Latent factor models (LFMs) such as matrix factorization achieve the state-of-the-art performance among various Collaborative Filtering (CF) approaches for recommendation.
A Neural Attention Model for Urban Air Quality Inference: Learning the Weights of Monitoring Stations
We leverage both the information from monitoring stations and urban data that are closely related to air quality, including POIs, road networks and meteorology.
