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Found 9 papers, 6 papers with code
Improving LLMs for Recommendation with Out-Of-Vocabulary Tokens
However, since LLMs are typically pretrained on natural language tasks, these in-vocabulary tokens lack the expressive power for distinctive users and items, thereby weakening the recommendation ability even after fine-tuning on recommendation tasks.
JobFormer: Skill-Aware Job Recommendation with Semantic-Enhanced Transformer
Moreover, we adopt a two-stage learning strategy for skill-aware recommendation, in which we utilize the skill distribution to guide JD representation learning in the recall stage, and then combine the user profiles for final prediction in the ranking stage.
Learning Operators with Stochastic Gradient Descent in General Hilbert Spaces
This study investigates leveraging stochastic gradient descent (SGD) to learn operators between general Hilbert spaces.
RE-SORT: Removing Spurious Correlation in Multilevel Interaction for CTR Prediction
Click-through rate (CTR) prediction is a critical task in recommendation systems, serving as the ultimate filtering step to sort items for a user.
COURIER: Contrastive User Intention Reconstruction for Large-Scale Visual Recommendation
Even if the performance of cross-modal prediction tasks is excellent, it is challenging to provide significant information gain for the downstream models.
IDToolkit: A Toolkit for Benchmarking and Developing Inverse Design Algorithms in Nanophotonics
These obstacles prevent AI researchers from developing specialized methods for scientific designs.
Generalized Delayed Feedback Model with Post-Click Information in Recommender Systems
We propose a generalized delayed feedback model (GDFM) that unifies both post-click behaviors and early conversions as stochastic post-click information, which could be utilized to train GDFM in a streaming manner efficiently.
RID-Noise: Towards Robust Inverse Design under Noisy Environments
We also define a sample-wise weight, which can be used in the maximum weighted likelihood estimation of an inverse model based on a cINN.
Capturing Delayed Feedback in Conversion Rate Prediction via Elapsed-Time Sampling
To strike a balance in this trade-off, we propose Elapsed-Time Sampling Delayed Feedback Model (ES-DFM), which models the relationship between the observed conversion distribution and the true conversion distribution.
