LKPNR: Large Language Models and Knowledge Graph for Personalized News Recommendation Framework

Accurately recommending candidate news to users is a basic challenge of personalized news recommendation systems.Traditional methods are usually difficult to learn and acquire complex semantic information in news texts,resulting in unsatisfactory recommendation results.Besides,these traditional methods are more friendly to active users with rich historical behaviors.However,they can not effectively solve the long tail problem of inactive users.To address these issues,this research presents a novel general framework that combines Large Language Models(LLM)and Knowledge Graphs(KG)into traditional methods.To learn the contextual information of news text,we use LLMs’powerful text understanding ability to generate news representations with rich semantic information,and then,the generated news representations are used to enhance the news encoding in traditional methods.In addition,multi-hops relationship of news entities is mined and the structural information of news is encoded using KG,thus alleviating the challenge of long-tail distribution.Experimental results demonstrate that compared with various traditional models,on evaluation indicators such as AUC,MRR,nDCG@5 and nDCG@10,the framework significantly improves the recommendation performance.The successful integration of LLM and KG in our framework has established a feasible way for achieving more accurate personalized news recommendation.Our code is available at https://github.com/Xuan-ZW/LKPNR.

Solar Active Region Magnetogram Generation by Attention Generative Adversarial Networks

Learning the mapping of magnetograms and EUV images is important for understanding the solar eruption mechanism and space weather forecasting.Previous works are mainly based on the pix2pix model for full-disk magnetograms generation and obtain good performance.However,in general,we are more concerned with the magnetic field distribution in the active regions where various solar storms such as the solar flare and coronal mass ejection happen.In this paper,we fuse the self-attention mechanism with the pix2pix model which allows more computation resource and greater weight for strong magnetic regions.In addition,the attention features are concatenated by the Residual Hadamard Production(RHP) with the abstracted features after the encoder.We named our model as RHP-attention pix2pix.From the experiments,we can find that the proposed model can generate magnetograms with finer strong magnetic structures,such as sunspots.In addition,the polarity distribution of generated magnetograms at strong magnetic regions is more consistent with observed ones.

A review of feature fusion-based media popularity prediction methods

With the popularization of social media,the way of information transmission has changed,and the prediction of information popularity based on social media platforms has attracted extensive attention.Feature fusion-based media popularity prediction methods focus on the multi-modal features of social media,which aim at exploring the key factors affecting media popularity.Meanwhile,the methods make up for the deficiency in feature utilization of traditional methods based on information propagation processes.In this paper,we review feature fusion-based media popularity prediction methods from the perspective of feature extraction and predictive model construction.Before that,we analyze the influencing factors of media popularity to provide intuitive understanding.We further argue about the advantages and disadvantages of existing methods and datasets to highlight the future directions.Finally,we discuss the applications of popularity prediction.To the best of our knowledge,this is the first survey reporting feature fusion-based media popularity prediction methods.