Probabilistic Latent Accumulated Relevance

Shengbo Guo (SoCS CECS)

CS HDR MONITORING AI Research Group

DATE: 2010-04-16
TIME: 10:50:00 - 11:20:00
LOCATION: RSISE Seminar Room, ground floor, building 115, cnr. North and Daley Roads, ANU
CONTACT: JavaScript must be enabled to display this email address.

ABSTRACT:
Diversity has been heavily motivated as an objective criterion for result sets in the information retrieval literature and various ad-hoc heuristics have been proposed to explicitly optimize for it. In this paper, we start from first principles and show that optimizing a simple criterion of set-based relevance in a latent variable graphical model --- a framework we refer to as probabilistic latent accumulated relevance (PLAR) --- leads to diversity as a naturally emph{emergent} property of the solution. PLAR emph{derives} variants of latent semantic indexing (LSI) kernels for relevance and diversity and does not require ad-hoc tuning parameters to balance them. PLAR also directly motivates the general form of many other ad-hoc diversity heuristics in the literature, albeit with important modifications that we show can lead to improved performance on a diversity testbed from the TREC 6-8 Interactive Track.

BIO:
PhD student in Computer Science. http://users.cecs.anu.edu.au/~sguo