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Found 7 papers, 4 papers with code
RecPrompt: A Prompt Tuning Framework for News Recommendation Using Large Language Models
In the evolving field of personalized news recommendation, understanding the semantics of the underlying data is crucial.
Large Language Models on Wikipedia-Style Survey Generation: an Evaluation in NLP Concepts
Educational materials such as survey articles in specialized fields like computer science traditionally require tremendous expert inputs and are therefore expensive to create and update.
Going Beyond Local: Global Graph-Enhanced Personalized News Recommendations
Precisely recommending candidate news articles to users has always been a core challenge for personalized news recommendation systems.
Topic-Centric Explanations for News Recommendation
News recommender systems (NRS) have been widely applied for online news websites to help users find relevant articles based on their interests.
Enhancing Topic Extraction in Recommender Systems with Entropy Regularization
In recent years, many recommender systems have utilized textual data for topic extraction to enhance interpretability.
A Novel Perspective to Look At Attention: Bi-level Attention-based Explainable Topic Modeling for News Classification
Many recent deep learning-based solutions have widely adopted the attention-based mechanism in various tasks of the NLP discipline.
BusTime: Which is the Right Prediction Model for My Bus Arrival Time?
With the rise of big data technologies, many smart transportation applications have been rapidly developed in recent years including bus arrival time predictions.
