Making medication prescriptions in response to the patient's diagnosis is a challenging task.The number of pharmaceutical companies,their inventory of medicines,and the recommended dosage confront a doctor with th...Making medication prescriptions in response to the patient's diagnosis is a challenging task.The number of pharmaceutical companies,their inventory of medicines,and the recommended dosage confront a doctor with the well-known problem of information and cognitive overload.To assist a medical practitioner in making informed decisions regarding a medical prescription to a patient,researchers have exploited electronic health records(EHRs)in automatically recommending medication.In recent years,medication recommendation using EHRs has been a salient research direction,which has attracted researchers to apply various deep learning(DL)models to the EHRs of patients in recommending prescriptions.Yet,in the absence of a holistic survey article,it needs a lot of effort and time to study these publications in order to understand the current state of research and identify the best-performing models along with the trends and challenges.To fill this research gap,this survey reports on state-of-the-art DL-based medication recommendation methods.It reviews the classification of DL-based medication recommendation(MR)models,compares their performance,and the unavoidable issues they face.It reports on the most common datasets and metrics used in evaluating MR models.The findings of this study have implications for researchers interested in MR models.展开更多
The traditional recommendation algorithm represented by the collaborative filtering algorithm is the most classical and widely recommended algorithm in the practical industry.Most book recommendation systems also use ...The traditional recommendation algorithm represented by the collaborative filtering algorithm is the most classical and widely recommended algorithm in the practical industry.Most book recommendation systems also use this algorithm.However,the traditional recommendation algorithm represented by the collaborative filtering algorithm cannot deal with the data sparsity well.This algorithm only uses the shallow feature design of the interaction between readers and books,so it fails to achieve the high-level abstract learning of the relevant attribute features of readers and books,leading to a decline in recommendation performance.Given the above problems,this study uses deep learning technology to model readers’book borrowing probability.It builds a recommendation system model through themulti-layer neural network and inputs the features extracted from readers and books into the network,and then profoundly integrates the features of readers and books through the multi-layer neural network.The hidden deep interaction between readers and books is explored accordingly.Thus,the quality of book recommendation performance will be significantly improved.In the experiment,the evaluation indexes ofHR@10,MRR,andNDCGof the deep neural network recommendation model constructed in this paper are higher than those of the traditional recommendation algorithm,which verifies the effectiveness of the model in the book recommendation.展开更多
Recommendation Information Systems(RIS)are pivotal in helping users in swiftly locating desired content from the vast amount of information available on the Internet.Graph Convolution Network(GCN)algorithms have been ...Recommendation Information Systems(RIS)are pivotal in helping users in swiftly locating desired content from the vast amount of information available on the Internet.Graph Convolution Network(GCN)algorithms have been employed to implement the RIS efficiently.However,the GCN algorithm faces limitations in terms of performance enhancement owing to the due to the embedding value-vanishing problem that occurs during the learning process.To address this issue,we propose a Weighted Forwarding method using the GCN(WF-GCN)algorithm.The proposed method involves multiplying the embedding results with different weights for each hop layer during graph learning.By applying the WF-GCN algorithm,which adjusts weights for each hop layer before forwarding to the next,nodes with many neighbors achieve higher embedding values.This approach facilitates the learning of more hop layers within the GCN framework.The efficacy of the WF-GCN was demonstrated through its application to various datasets.In the MovieLens dataset,the implementation of WF-GCN in LightGCN resulted in significant performance improvements,with recall and NDCG increasing by up to+163.64%and+132.04%,respectively.Similarly,in the Last.FM dataset,LightGCN using WF-GCN enhanced with WF-GCN showed substantial improvements,with the recall and NDCG metrics rising by up to+174.40%and+169.95%,respectively.Furthermore,the application of WF-GCN to Self-supervised Graph Learning(SGL)and Simple Graph Contrastive Learning(SimGCL)also demonstrated notable enhancements in both recall and NDCG across these datasets.展开更多
近年来,用户兴趣点(point of interest,POI)推荐是基于位置的社会网络(location-based social network,LBSN)研究的热门话题,POI推荐不仅可以帮助用户找到心仪的POI,也可为商家带来可观收益。深度学习技术因可以更有效地捕获用户与物品...近年来,用户兴趣点(point of interest,POI)推荐是基于位置的社会网络(location-based social network,LBSN)研究的热门话题,POI推荐不仅可以帮助用户找到心仪的POI,也可为商家带来可观收益。深度学习技术因可以更有效地捕获用户与物品间的非线性关系,逐渐应用到推荐系统任务中。对近年来结合深度学习技术的用户POI推荐的研究进行综述。首先介绍了用户POI推荐与传统推荐任务的区别,并介绍了可以提高推荐任务模型性能的多种影响因素;随后将深度学习应用到POI推荐的方式分为4类:POI的向量化学习、深度协同过滤、从辅助内容中提取特征和利用循环神经网络进行序列推荐,并阐述了深度学习技术在这些方式中的应用效果与优势;最后对结合深度学习技术的用户POI推荐的发展方向进行了总结与展望。展开更多
基金funded by Southeast University-China Mobile Research Institute Joint Innovation Center undergrantno.CMYJY-202200475。
文摘Making medication prescriptions in response to the patient's diagnosis is a challenging task.The number of pharmaceutical companies,their inventory of medicines,and the recommended dosage confront a doctor with the well-known problem of information and cognitive overload.To assist a medical practitioner in making informed decisions regarding a medical prescription to a patient,researchers have exploited electronic health records(EHRs)in automatically recommending medication.In recent years,medication recommendation using EHRs has been a salient research direction,which has attracted researchers to apply various deep learning(DL)models to the EHRs of patients in recommending prescriptions.Yet,in the absence of a holistic survey article,it needs a lot of effort and time to study these publications in order to understand the current state of research and identify the best-performing models along with the trends and challenges.To fill this research gap,this survey reports on state-of-the-art DL-based medication recommendation methods.It reviews the classification of DL-based medication recommendation(MR)models,compares their performance,and the unavoidable issues they face.It reports on the most common datasets and metrics used in evaluating MR models.The findings of this study have implications for researchers interested in MR models.
基金This work was partly supported by the Basic Ability Improvement Project for Young andMiddle-aged Teachers in Guangxi Colleges andUniversities(2021KY1800,2021KY1804).
文摘The traditional recommendation algorithm represented by the collaborative filtering algorithm is the most classical and widely recommended algorithm in the practical industry.Most book recommendation systems also use this algorithm.However,the traditional recommendation algorithm represented by the collaborative filtering algorithm cannot deal with the data sparsity well.This algorithm only uses the shallow feature design of the interaction between readers and books,so it fails to achieve the high-level abstract learning of the relevant attribute features of readers and books,leading to a decline in recommendation performance.Given the above problems,this study uses deep learning technology to model readers’book borrowing probability.It builds a recommendation system model through themulti-layer neural network and inputs the features extracted from readers and books into the network,and then profoundly integrates the features of readers and books through the multi-layer neural network.The hidden deep interaction between readers and books is explored accordingly.Thus,the quality of book recommendation performance will be significantly improved.In the experiment,the evaluation indexes ofHR@10,MRR,andNDCGof the deep neural network recommendation model constructed in this paper are higher than those of the traditional recommendation algorithm,which verifies the effectiveness of the model in the book recommendation.
基金This work was supported by the Kyonggi University Research Grant 2022.
文摘Recommendation Information Systems(RIS)are pivotal in helping users in swiftly locating desired content from the vast amount of information available on the Internet.Graph Convolution Network(GCN)algorithms have been employed to implement the RIS efficiently.However,the GCN algorithm faces limitations in terms of performance enhancement owing to the due to the embedding value-vanishing problem that occurs during the learning process.To address this issue,we propose a Weighted Forwarding method using the GCN(WF-GCN)algorithm.The proposed method involves multiplying the embedding results with different weights for each hop layer during graph learning.By applying the WF-GCN algorithm,which adjusts weights for each hop layer before forwarding to the next,nodes with many neighbors achieve higher embedding values.This approach facilitates the learning of more hop layers within the GCN framework.The efficacy of the WF-GCN was demonstrated through its application to various datasets.In the MovieLens dataset,the implementation of WF-GCN in LightGCN resulted in significant performance improvements,with recall and NDCG increasing by up to+163.64%and+132.04%,respectively.Similarly,in the Last.FM dataset,LightGCN using WF-GCN enhanced with WF-GCN showed substantial improvements,with the recall and NDCG metrics rising by up to+174.40%and+169.95%,respectively.Furthermore,the application of WF-GCN to Self-supervised Graph Learning(SGL)and Simple Graph Contrastive Learning(SimGCL)also demonstrated notable enhancements in both recall and NDCG across these datasets.
文摘近年来,用户兴趣点(point of interest,POI)推荐是基于位置的社会网络(location-based social network,LBSN)研究的热门话题,POI推荐不仅可以帮助用户找到心仪的POI,也可为商家带来可观收益。深度学习技术因可以更有效地捕获用户与物品间的非线性关系,逐渐应用到推荐系统任务中。对近年来结合深度学习技术的用户POI推荐的研究进行综述。首先介绍了用户POI推荐与传统推荐任务的区别,并介绍了可以提高推荐任务模型性能的多种影响因素;随后将深度学习应用到POI推荐的方式分为4类:POI的向量化学习、深度协同过滤、从辅助内容中提取特征和利用循环神经网络进行序列推荐,并阐述了深度学习技术在这些方式中的应用效果与优势;最后对结合深度学习技术的用户POI推荐的发展方向进行了总结与展望。
