Coterie是一种异步的组模式,要求在不等时间间隔约束下,找出具有相似轨迹行为的组模式.而传统的轨迹组模式挖掘算法往往处理具有固定时间间隔采样约束的GPS数据,因此无法直接用于Coterie模式挖掘.同时,传统组模式挖掘存在语义信息缺失问...Coterie是一种异步的组模式,要求在不等时间间隔约束下,找出具有相似轨迹行为的组模式.而传统的轨迹组模式挖掘算法往往处理具有固定时间间隔采样约束的GPS数据,因此无法直接用于Coterie模式挖掘.同时,传统组模式挖掘存在语义信息缺失问题,降低了个性化旅游路线推荐的完整度和准确度.为此,提出基于语义的距离敏感推荐策略DRSS(distance-aware recommendation strategy based on semantics)和基于语义的从众性推荐策略CRSS(conformity-aware recommendation strategy based on semantics).此外,随着社交网数据规模的不断增大,传统组模式聚类算法的效率受到极大的挑战,因此,为了高效处理大规模社交网轨迹数据,使用带有优化聚类的MapReduce编程模型来挖掘Coterie组模式.实验结果表明:MapReduce编程模型下带优化聚类和语义信息的Coterie组模式挖掘,在个性化旅游路线推荐上优于传统组模式旅游路线推荐质量,且能够有效处理大规模社交网轨迹数据.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Coterie是一种异步的组模式,要求在不等时间间隔约束下,找出具有相似轨迹行为的组模式.而传统的轨迹组模式挖掘算法往往处理具有固定时间间隔采样约束的GPS数据,因此无法直接用于Coterie模式挖掘.同时,传统组模式挖掘存在语义信息缺失问题,降低了个性化旅游路线推荐的完整度和准确度.为此,提出基于语义的距离敏感推荐策略DRSS(distance-aware recommendation strategy based on semantics)和基于语义的从众性推荐策略CRSS(conformity-aware recommendation strategy based on semantics).此外,随着社交网数据规模的不断增大,传统组模式聚类算法的效率受到极大的挑战,因此,为了高效处理大规模社交网轨迹数据,使用带有优化聚类的MapReduce编程模型来挖掘Coterie组模式.实验结果表明:MapReduce编程模型下带优化聚类和语义信息的Coterie组模式挖掘,在个性化旅游路线推荐上优于传统组模式旅游路线推荐质量,且能够有效处理大规模社交网轨迹数据.
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%.
