Efficient Computation of Personal Aggregate Queries on Blogs

摘要

There is an exploding amount of user-generated content on the Web due to the emergence of "Web 2.0" services, such as Blogger, MyS-pace, Flickr, and del.icio.us. The participation of a large number of users in sharing their opinion on the Web has inspired researchers to build an effective "information filter" by aggregating these independent opinions. However, given the diverse groups of users on the Web nowadays, the global aggregation of the information may not be of much interest to different groups of users. In this paper, we explore the possibility of computing personalized aggregation over the opinions expressed on the Web based on a user's indication of trust over the information sources. The hope is that by employing such "personalized" aggregation, we can make the recommendation more likely to be interesting to the users. We 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. We 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.