洗发液要二合一，咖啡要三合一，手机也要多合一，只要是越方便快捷就越好，融合已经是不变的发展趁势了，即使是一直讲求品质的影音器材也不例外。曾经需要逐样分开买的影音产品也可以多合一，Optoma Movie Time DV10投影机就是这样一...洗发液要二合一，咖啡要三合一，手机也要多合一，只要是越方便快捷就越好，融合已经是不变的发展趁势了，即使是一直讲求品质的影音器材也不例外。曾经需要逐样分开买的影音产品也可以多合一，Optoma Movie Time DV10投影机就是这样一款产品。展开更多
With the eruption of big data,practical recommendation schemes are now very important in various fields,including e-commerce,social networks,and a number of web-based services.Nowadays,there exist many personalized mo...With the eruption of big data,practical recommendation schemes are now very important in various fields,including e-commerce,social networks,and a number of web-based services.Nowadays,there exist many personalized movie recommendation schemes utilizing publicly available movie datasets(e.g.,MovieLens and Netflix),and returning improved performance metrics(e.g.,Root-Mean-Square Error(RMSE)).However,two fundamental issues faced by movie recommendation systems are still neglected:first,scalability,and second,practical usage feedback and verification based on real implementation.In particular,Collaborative Filtering(CF)is one of the major prevailing techniques for implementing recommendation systems.However,traditional CF schemes suffer from a time complexity problem,which makes them bad candidates for real-world recommendation systems.In this paper,we address these two issues.Firstly,a simple but high-efficient recommendation algorithm is proposed,which exploits users1 profile attributes to partition them into several clusters.For each cluster,a virtual opinion leader is conceived to represent the whole cluster,such that the dimension of the original useritem matrix can be significantly reduced,then a Weighted Slope One-VU method is designed and applied to the virtual opinion leader-item matrix to obtain the recommendation results.Compared to traditional clusteringbased CF recommendation schemes,our method can significantly reduce the time complexity,while achieving comparable recommendation performance.Furthermore,we have constructed a real personalized web-based movie recommendation system,MovieWatch,opened it to the public,collected user feedback on recommendations,and evaluated the feasibility and accuracy of our system based on this real-world data.展开更多
文摘With the eruption of big data,practical recommendation schemes are now very important in various fields,including e-commerce,social networks,and a number of web-based services.Nowadays,there exist many personalized movie recommendation schemes utilizing publicly available movie datasets(e.g.,MovieLens and Netflix),and returning improved performance metrics(e.g.,Root-Mean-Square Error(RMSE)).However,two fundamental issues faced by movie recommendation systems are still neglected:first,scalability,and second,practical usage feedback and verification based on real implementation.In particular,Collaborative Filtering(CF)is one of the major prevailing techniques for implementing recommendation systems.However,traditional CF schemes suffer from a time complexity problem,which makes them bad candidates for real-world recommendation systems.In this paper,we address these two issues.Firstly,a simple but high-efficient recommendation algorithm is proposed,which exploits users1 profile attributes to partition them into several clusters.For each cluster,a virtual opinion leader is conceived to represent the whole cluster,such that the dimension of the original useritem matrix can be significantly reduced,then a Weighted Slope One-VU method is designed and applied to the virtual opinion leader-item matrix to obtain the recommendation results.Compared to traditional clusteringbased CF recommendation schemes,our method can significantly reduce the time complexity,while achieving comparable recommendation performance.Furthermore,we have constructed a real personalized web-based movie recommendation system,MovieWatch,opened it to the public,collected user feedback on recommendations,and evaluated the feasibility and accuracy of our system based on this real-world data.
