算法推荐服务是指利用生成合成类、个性化推送类、排序精选类、检索过滤类、调度决策类等算法技术向用户提供信息的服务。其在满足人们多元化需求的同时也产生了技术异化、信息失真泄漏、欺诈顾客等风险,对电子商务发展具有不可小觑的打...算法推荐服务是指利用生成合成类、个性化推送类、排序精选类、检索过滤类、调度决策类等算法技术向用户提供信息的服务。其在满足人们多元化需求的同时也产生了技术异化、信息失真泄漏、欺诈顾客等风险,对电子商务发展具有不可小觑的打击,应从多个维度协同治理。随着《电子商务法》的出台,以期构建生态环境良好的互联网信息服务环境,规制滥用算法推荐技术的法律风险。Algorithmic recommendation service refers to a service that provides information to users using algorithm technologies such as generative synthesis, personalized push, sorting and selection, retrieval and filtering, scheduling and decision-making. While meeting people’s diverse needs, it also generates risks such as technological alienation, information distortion and leakage, and customer fraud, which have a significant impact on the development of e-commerce and should be addressed through collaborative governance from multiple dimensions. With the promulgation of the Electronic Commerce Law, it is expected to build an Internet information service environment with a good ecological environment and regulate the legal risk of abusing algorithm recommendation technology.展开更多
With the development of the Internet of Things(Io T), people's lives have become increasingly convenient. It is desirable for smart home(SH) systems to integrate and leverage the enormous information available fro...With the development of the Internet of Things(Io T), people's lives have become increasingly convenient. It is desirable for smart home(SH) systems to integrate and leverage the enormous information available from IoT. Information can be analyzed to learn user intentions and automatically provide the appropriate services. However, existing service recommendation models typically do not consider the services that are unavailable in a user's living environment. In order to address this problem, we propose a series of semantic models for SH devices. These semantic models can be used to infer user intentions. Based on the models, we proposed a service recommendation probability model and an alternative-service recommending algorithm. The algorithm is devoted to providing appropriate alternative services when the desired service is unavailable. The algorithm has been implemented and achieves accuracy higher than traditional Hidden Markov Model(HMM). The maximum accuracy achieved is 68.3%.展开更多
文摘算法推荐服务是指利用生成合成类、个性化推送类、排序精选类、检索过滤类、调度决策类等算法技术向用户提供信息的服务。其在满足人们多元化需求的同时也产生了技术异化、信息失真泄漏、欺诈顾客等风险,对电子商务发展具有不可小觑的打击,应从多个维度协同治理。随着《电子商务法》的出台,以期构建生态环境良好的互联网信息服务环境,规制滥用算法推荐技术的法律风险。Algorithmic recommendation service refers to a service that provides information to users using algorithm technologies such as generative synthesis, personalized push, sorting and selection, retrieval and filtering, scheduling and decision-making. While meeting people’s diverse needs, it also generates risks such as technological alienation, information distortion and leakage, and customer fraud, which have a significant impact on the development of e-commerce and should be addressed through collaborative governance from multiple dimensions. With the promulgation of the Electronic Commerce Law, it is expected to build an Internet information service environment with a good ecological environment and regulate the legal risk of abusing algorithm recommendation technology.
基金supported by the National Key Research and Development Program(No.2016YFB0800302)
文摘With the development of the Internet of Things(Io T), people's lives have become increasingly convenient. It is desirable for smart home(SH) systems to integrate and leverage the enormous information available from IoT. Information can be analyzed to learn user intentions and automatically provide the appropriate services. However, existing service recommendation models typically do not consider the services that are unavailable in a user's living environment. In order to address this problem, we propose a series of semantic models for SH devices. These semantic models can be used to infer user intentions. Based on the models, we proposed a service recommendation probability model and an alternative-service recommending algorithm. The algorithm is devoted to providing appropriate alternative services when the desired service is unavailable. The algorithm has been implemented and achieves accuracy higher than traditional Hidden Markov Model(HMM). The maximum accuracy achieved is 68.3%.
