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Found 8 papers, 7 papers with code
MIND Your Language: A Multilingual Dataset for Cross-lingual News Recommendation
Our findings reveal that (i) current NNRs, even when based on a multilingual language model, suffer from substantial performance losses under ZS-XLT and that (ii) inclusion of target-language data in FS-XLT training has limited benefits, particularly when combined with a bilingual news consumption.
NewsRecLib: A PyTorch-Lightning Library for Neural News Recommendation
NewsRecLib is an open-source library based on Pytorch-Lightning and Hydra developed for training and evaluating neural news recommendation models.
NeMig -- A Bilingual News Collection and Knowledge Graph about Migration
News recommendation plays a critical role in shaping the public's worldviews through the way in which it filters and disseminates information about different topics.
Train Once, Use Flexibly: A Modular Framework for Multi-Aspect Neural News Recommendation
Recent neural news recommenders (NNRs) extend content-based recommendation (1) by aligning additional aspects (e. g., topic, sentiment) between candidate news and user history or (2) by diversifying recommendations w. r. t.
Simplifying Content-Based Neural News Recommendation: On User Modeling and Training Objectives
Most neural news recommenders rely on user click behavior and typically introduce dedicated user encoders that aggregate the content of clicked news into user embeddings (early fusion).
Towards Analyzing the Bias of News Recommender Systems Using Sentiment and Stance Detection
News recommender systems are used by online news providers to alleviate information overload and to provide personalized content to users.
GraphConfRec: A Graph Neural Network-Based Conference Recommender System
In today's academic publishing model, especially in Computer Science, conferences commonly constitute the main platforms for releasing the latest peer-reviewed advancements in their respective fields.
More is not Always Better: The Negative Impact of A-box Materialization on RDF2vec Knowledge Graph Embeddings
RDF2vec is an embedding technique for representing knowledge graph entities in a continuous vector space.
