Open Collections

SUE : an advertisement recommendation framework utilizing categorized events and stimuli

University of British Columbia

With the emergence of peer-to-peer video-on-demand systems, new avenues for keeping track of and subsequently meeting user needs and desires have arisen. Based on the idea of contextual priming, we introduce a new frame-work, SUE, that takes advantage of the intimate level of user profiling afforded by the internet as well as the linear and segmented nature of p2p technology to determine a user's exact on-screen experience at any given time interval. This allows us to more accurately determine the type of information a user is likely to be more receptive to. Our design differs from other existing systems in two ways: (a) the level of granularity it can support, accommodating factors from both the user's on-screen and physical environment in making its recommendations and (b) in addressing some of the shortcomings seen in current applications, such as those imposed by coarse user profiling and faulty associations. In order to examine the viability of our framework, we provide a high level design specifying its incorporation into an existing p2p video system, the BitVampire project.

Thesis/Dissertation

application/pdf

2008-10-02

Attribution-NonCommercial-NoDerivatives 4.0 International

http://hdl.handle.net/2429/2450

Computer Science

University of British Columbia

UBCV

http://creativecommons.org/licenses/by-nc-nd/4.0/