The digital technologies that run based on users’content provide a platform for users to help air their opinions on various aspects of a particular subject or product.The recommendation agents play a crucial role in ...The digital technologies that run based on users’content provide a platform for users to help air their opinions on various aspects of a particular subject or product.The recommendation agents play a crucial role in personalizing the needs of individual users.Therefore,it is essential to improve the user experience.The recommender system focuses on recommending a set of items to a user to help the decision-making process and is prevalent across e-commerce and media websites.In Context-Aware Recommender Systems(CARS),several influential and contextual variables are identified to provide an effective recommendation.A substantial trade-off is applied in context to achieve the proper accuracy and coverage required for a collaborative recommendation.The CARS will generate more recommendations utilizing adapting them to a certain contextual situation of users.However,the key issue is how contextual information is used to create good and intelligent recommender systems.This paper proposes an Artificial Neural Network(ANN)to achieve contextual recommendations based on usergenerated reviews.The ability of ANNs to learn events and make decisions based on similar events makes it effective for personalized recommendations in CARS.Thus,the most appropriate contexts in which a user should choose an item or service are achieved.This work converts every label set into a Multi-Label Classification(MLC)problem to enhance recommendations.Experimental results show that the proposed ANN performs better in the Binary Relevance(BR)Instance-Based Classifier,the BR Decision Tree,and the Multi-label SVM for Trip Advisor and LDOS-CoMoDa Dataset.Furthermore,the accuracy of the proposed ANN achieves better results by 1.1%to 6.1%compared to other existing methods.展开更多
The term“undruggable”is to describe molecules that are not targetable or at least hard to target pharmacologically.Unfortunately,some targets with potent oncogenic activity fall into this category,and currently litt...The term“undruggable”is to describe molecules that are not targetable or at least hard to target pharmacologically.Unfortunately,some targets with potent oncogenic activity fall into this category,and currently little is known about how to solve this problem,which largely hampered drug research on human cancers.Ras,as one of the most common oncogenes,was previously considered“undruggable”,but in recent years,a few small molecules like Sotorasib(AMG-510)have emerged and proved their targeted anti-cancer effects.Further,myc,as one of the most studied oncogenes,and tp53,being the most common tumor suppressor genes,are both considered“undruggable”.Many attempts have been made to target these“undruggable”targets,but little progress has been made yet.This article summarizes the current progress of direct and indirect targeting approaches for ras,myc,two oncogenes,and tp53,a tumor suppressor gene.These are potential therapeutic targets but are considered“undruggable”.We conclude with some emerging research approaches like proteolysis targeting chimeras(PROTACs),cancer vaccines,and artificial intelligence(AI)-based drug discovery,which might provide new cues for cancer intervention.Therefore,this review sets out to clarify the current status of targeted anti-cancer drug research,and the insights gained from this review may be of assistance to learn from experience and find new ideas in developing new chemicals that directly target such“undruggable”molecules.展开更多
选址作为商业决策和城市基础设施规划的核心环节,对实体店铺、城市基础设施能否发挥预期效用具有重要作用.现有的选址推荐系统数据服务编排较为固定,无法对不同用户需求系统做出及时调整,应用场景受限,人机交互的系统灵活性和可扩展性差...选址作为商业决策和城市基础设施规划的核心环节,对实体店铺、城市基础设施能否发挥预期效用具有重要作用.现有的选址推荐系统数据服务编排较为固定,无法对不同用户需求系统做出及时调整,应用场景受限,人机交互的系统灵活性和可扩展性差.最近,以GPT-4为代表的大语言模型(large language model,LLM)展现出了强大的意图理解、任务编排、代码生成和工具使用能力,能够完成传统推荐模型难以兼顾的任务,为重塑推荐流程、实现一体化的推荐服务提供了新的机遇.然而,一方面选址推荐兼具传统推荐共有的挑战;另一方面,由于其基于空间数据,具有独特的挑战.在这一背景下,提出了大语言模型驱动的选址推荐系统.首先,拓展了选址推荐的场景,提出了根据位置寻找合适店铺类型的场景推荐任务,结合了协同过滤算法和空间预训练模型.其次,构建了由大语言模型驱动的选址决策引擎.语言模型本身在处理空间相关的任务上存在诸多缺陷,例如缺少空间感知能力、无法理解具体位置、会虚构地名地址等.提出了一种在语言模型框架处理空间任务的机制,通过地理编码、逆编码、地名地址解析等工具提升模型的空间感知能力并避免地址虚构问题,结合选址推荐模型、场景推荐模型、外部知识库、地图可视化完成选址推荐中的多样化任务.实现选址任务的智能规划、执行与归因,提升了空间服务系统的交互体验,为未来人工智能驱动的选址推荐系统提供新的设计和实现思路.展开更多
文摘The digital technologies that run based on users’content provide a platform for users to help air their opinions on various aspects of a particular subject or product.The recommendation agents play a crucial role in personalizing the needs of individual users.Therefore,it is essential to improve the user experience.The recommender system focuses on recommending a set of items to a user to help the decision-making process and is prevalent across e-commerce and media websites.In Context-Aware Recommender Systems(CARS),several influential and contextual variables are identified to provide an effective recommendation.A substantial trade-off is applied in context to achieve the proper accuracy and coverage required for a collaborative recommendation.The CARS will generate more recommendations utilizing adapting them to a certain contextual situation of users.However,the key issue is how contextual information is used to create good and intelligent recommender systems.This paper proposes an Artificial Neural Network(ANN)to achieve contextual recommendations based on usergenerated reviews.The ability of ANNs to learn events and make decisions based on similar events makes it effective for personalized recommendations in CARS.Thus,the most appropriate contexts in which a user should choose an item or service are achieved.This work converts every label set into a Multi-Label Classification(MLC)problem to enhance recommendations.Experimental results show that the proposed ANN performs better in the Binary Relevance(BR)Instance-Based Classifier,the BR Decision Tree,and the Multi-label SVM for Trip Advisor and LDOS-CoMoDa Dataset.Furthermore,the accuracy of the proposed ANN achieves better results by 1.1%to 6.1%compared to other existing methods.
基金supported by Grants from the National Natural Science Foundation of China(81902784)the CAMS Innovation Fund for Medical Sciences(CIFMS,2019-I2M-5-004)+2 种基金the Fund of Sichuan Provincial Department of Science and Technology(2022YFSY0058)the Research Funding(RCDWJS 2020-20)the Research and Development Program(RD-02-202002)from West China School/Hospital of Stomatology Sichuan University.
文摘The term“undruggable”is to describe molecules that are not targetable or at least hard to target pharmacologically.Unfortunately,some targets with potent oncogenic activity fall into this category,and currently little is known about how to solve this problem,which largely hampered drug research on human cancers.Ras,as one of the most common oncogenes,was previously considered“undruggable”,but in recent years,a few small molecules like Sotorasib(AMG-510)have emerged and proved their targeted anti-cancer effects.Further,myc,as one of the most studied oncogenes,and tp53,being the most common tumor suppressor genes,are both considered“undruggable”.Many attempts have been made to target these“undruggable”targets,but little progress has been made yet.This article summarizes the current progress of direct and indirect targeting approaches for ras,myc,two oncogenes,and tp53,a tumor suppressor gene.These are potential therapeutic targets but are considered“undruggable”.We conclude with some emerging research approaches like proteolysis targeting chimeras(PROTACs),cancer vaccines,and artificial intelligence(AI)-based drug discovery,which might provide new cues for cancer intervention.Therefore,this review sets out to clarify the current status of targeted anti-cancer drug research,and the insights gained from this review may be of assistance to learn from experience and find new ideas in developing new chemicals that directly target such“undruggable”molecules.
文摘选址作为商业决策和城市基础设施规划的核心环节,对实体店铺、城市基础设施能否发挥预期效用具有重要作用.现有的选址推荐系统数据服务编排较为固定,无法对不同用户需求系统做出及时调整,应用场景受限,人机交互的系统灵活性和可扩展性差.最近,以GPT-4为代表的大语言模型(large language model,LLM)展现出了强大的意图理解、任务编排、代码生成和工具使用能力,能够完成传统推荐模型难以兼顾的任务,为重塑推荐流程、实现一体化的推荐服务提供了新的机遇.然而,一方面选址推荐兼具传统推荐共有的挑战;另一方面,由于其基于空间数据,具有独特的挑战.在这一背景下,提出了大语言模型驱动的选址推荐系统.首先,拓展了选址推荐的场景,提出了根据位置寻找合适店铺类型的场景推荐任务,结合了协同过滤算法和空间预训练模型.其次,构建了由大语言模型驱动的选址决策引擎.语言模型本身在处理空间相关的任务上存在诸多缺陷,例如缺少空间感知能力、无法理解具体位置、会虚构地名地址等.提出了一种在语言模型框架处理空间任务的机制,通过地理编码、逆编码、地名地址解析等工具提升模型的空间感知能力并避免地址虚构问题,结合选址推荐模型、场景推荐模型、外部知识库、地图可视化完成选址推荐中的多样化任务.实现选址任务的智能规划、执行与归因,提升了空间服务系统的交互体验,为未来人工智能驱动的选址推荐系统提供新的设计和实现思路.
