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Found 10 papers, 3 papers with code
GraphTranslator: Aligning Graph Model to Large Language Model for Open-ended Tasks
Large language models (LLMs) like ChatGPT, exhibit powerful zero-shot and instruction-following capabilities, have catalyzed a revolutionary transformation across diverse fields, especially for open-ended tasks.
GraphGPT: Graph Learning with Generative Pre-trained Transformers
We introduce \textit{GraphGPT}, a novel model for Graph learning by self-supervised Generative Pre-training Transformers.
Uniform Sequence Better: Time Interval Aware Data Augmentation for Sequential Recommendation
However, we observe that the time interval in a sequence may vary significantly different, and thus result in the ineffectiveness of user modeling due to the issue of \emph{preference drift}.
Gating-adapted Wavelet Multiresolution Analysis for Exposure Sequence Modeling in CTR prediction
The exposure sequence is being actively studied for user interest modeling in Click-Through Rate (CTR) prediction.
Alleviating Cold-start Problem in CTR Prediction with A Variational Embedding Learning Framework
We propose a general Variational Embedding Learning Framework (VELF) for alleviating the severe cold-start problem in CTR prediction.
Empowering General-purpose User Representation with Full-life Cycle Behavior Modeling
User Modeling plays an essential role in industry.
Learning Universal User Representations via Self-Supervised Lifelong Behaviors Modeling
However, existing researches have little ability to model universal user representation based on lifelong behavior sequences since user registration.
Interest-oriented Universal User Representation via Contrastive Learning
Universal user representation has received many interests recently, with which we can be free from the cumbersome work of training a specific model for each downstream application.
Exploiting Behavioral Consistence for Universal User Representation
In this work, we focus on developing universal user representation model.
Zero-shot Text Classification via Reinforced Self-training
In this situation, transferring from seen classes to unseen classes is extremely hard.
