EBSN(Event-based Social Networks)与传统社交网络有所不同,它不仅包含传统社交网中的线上交互(Online Interactions),还包含颇具价值的线下交互(Offline Interactions),是一种异构型复杂社交网络。如何有效利用这种虚拟与物理相融合...EBSN(Event-based Social Networks)与传统社交网络有所不同,它不仅包含传统社交网中的线上交互(Online Interactions),还包含颇具价值的线下交互(Offline Interactions),是一种异构型复杂社交网络。如何有效利用这种虚拟与物理相融合的交互关系来提高活动推荐服务的质量,是目前学术界和工业界共同关注的热点研究问题之一。传统社交活动推荐算法,如基于用户偏好或线上好友关系的活动推荐算法,除了考虑活动和用户的基本属性外,大多基于显式好友关系EF(Explicit Friendship)进行活动推荐,但EBSN不具备显式好友关系,因此上述算法均不能直接用于EBSN活动推荐。为此,定义了一种新的潜在好友关系LF(Latent Friendship),LF关系将线上同组、线下同活动综合纳入活动评分计算中,以体现LF对EBSN活动推荐的影响;同时,基于此提出了一种基于潜在好友关系的EBSN活动推荐算法(Activity Recommendation Algorithm based on Latent Friendships,ARLF),该算法在寻找潜在好友关系时,创新性地运用元路径思想,使得EBSN中的异构信息得到了充分利用。最后,利用Meetup事件社交网中的真实数据对ARLF算法进行了性能测试,可扩展性实验证明了该算法是可行且有效的。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
针对专业领域问答系统中推荐专家回答不准确与不及时的问题,提出一种基于兴趣度、权威度、信誉度和最近活跃度的专家推荐混合模型。采用加权的LDA主题模型获得专家兴趣主题分布,采用基于主题的PageRank算法计算专家的权威度;根据专家回...针对专业领域问答系统中推荐专家回答不准确与不及时的问题,提出一种基于兴趣度、权威度、信誉度和最近活跃度的专家推荐混合模型。采用加权的LDA主题模型获得专家兴趣主题分布,采用基于主题的PageRank算法计算专家的权威度;根据专家回答问题的质量计算专家的信誉度,根据专家历史回答问题的时间获得专家的最近活跃度。给出用户问题的分析方法,采用混合模型推荐最适宜的问题服务专家。为了验证模型的可行性和有效性,使用Stack Over Flow真实数据集进行分析实验。实验结果表明该方法能够有效地提高新问题专家推荐的准确率。展开更多
文摘EBSN(Event-based Social Networks)与传统社交网络有所不同,它不仅包含传统社交网中的线上交互(Online Interactions),还包含颇具价值的线下交互(Offline Interactions),是一种异构型复杂社交网络。如何有效利用这种虚拟与物理相融合的交互关系来提高活动推荐服务的质量,是目前学术界和工业界共同关注的热点研究问题之一。传统社交活动推荐算法,如基于用户偏好或线上好友关系的活动推荐算法,除了考虑活动和用户的基本属性外,大多基于显式好友关系EF(Explicit Friendship)进行活动推荐,但EBSN不具备显式好友关系,因此上述算法均不能直接用于EBSN活动推荐。为此,定义了一种新的潜在好友关系LF(Latent Friendship),LF关系将线上同组、线下同活动综合纳入活动评分计算中,以体现LF对EBSN活动推荐的影响;同时,基于此提出了一种基于潜在好友关系的EBSN活动推荐算法(Activity Recommendation Algorithm based on Latent Friendships,ARLF),该算法在寻找潜在好友关系时,创新性地运用元路径思想,使得EBSN中的异构信息得到了充分利用。最后,利用Meetup事件社交网中的真实数据对ARLF算法进行了性能测试,可扩展性实验证明了该算法是可行且有效的。
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%.
文摘针对专业领域问答系统中推荐专家回答不准确与不及时的问题,提出一种基于兴趣度、权威度、信誉度和最近活跃度的专家推荐混合模型。采用加权的LDA主题模型获得专家兴趣主题分布,采用基于主题的PageRank算法计算专家的权威度;根据专家回答问题的质量计算专家的信誉度,根据专家历史回答问题的时间获得专家的最近活跃度。给出用户问题的分析方法,采用混合模型推荐最适宜的问题服务专家。为了验证模型的可行性和有效性,使用Stack Over Flow真实数据集进行分析实验。实验结果表明该方法能够有效地提高新问题专家推荐的准确率。
