Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: There is an exploding amount of user-generated content on theb due to the emergence of "b 2.0" services, such as Blogger,MySpace, Flickr, and del.icio.us. The participation of a large number of users in sharing their opinion on the b has inspired researchers to build an effective "information filter" by aggregating these independent opinions. Hover, given the diverse groups of users on the b nowadays, the global aggregation of the information may not be of much interest to different groups of users. ,  explore the possibility of computing personalized aggregation over the opinions expressed on the b based on a user's indication of trust over the information sources. The hope is that by employing such "personalized" aggregation,  can make the recommendation more likely to be interesting to the users.  address the challenging scalability issues by proposing an efficient method, that utilizes two core techniques:  Non-Negative Matrix Factorization  and  Threshold Algorithm , to compute personalized aggregations when there are potentially millions of users and millions of sources within a system.  show that, through experiments on real-life dataset, our personalized aggregation approach indeed makes a significant difference in the items that are recommended and it reduces the query computational cost significantly, often more than 75%, while the result of personalized aggregation is kept accurate enough.
['Ka Cheung Sia', 'Junghoo Cho', 'Yun Chi', 'Belle L. Tseng']
I am writing a research paper on a idea Efficient computation of personal aggregate queries on blogs
[{"from": "human", "value": "I am writing a research paper on a idea Efficient computation of personal aggregate queries on blogs"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: There is an exploding amount of user-generated content on theb due to the emergence of \"b 2.0\" services, such as Blogger,MySpace, Flickr, and del.icio.us. The participation of a large number of users in sharing their opinion on the b has inspired researchers to build an effective \"information filter\" by aggregating these independent opinions. Hover, given the diverse groups of users on the b nowadays, the global aggregation of the information may not be of much interest to different groups of users. ,  explore the possibility of computing personalized aggregation over the opinions expressed on the b based on a user's indication of trust over the information sources. The hope is that by employing such \"personalized\" aggregation,  can make the recommendation more likely to be interesting to the users.  address the challenging scalability issues by proposing an efficient method, that utilizes two core techniques:  Non-Negative Matrix Factorization  and  Threshold Algorithm , to compute personalized aggregations when there are potentially millions of users and millions of sources within a system.  show that, through experiments on real-life dataset, our personalized aggregation approach indeed makes a significant difference in the items that are recommended and it reduces the query computational cost significantly, often more than 75%, while the result of personalized aggregation is kept accurate enough."}]