How to grow more pairs: suggesting review targets for comparison-friendly review ecosystems
Venue
WWW (2013), pp. 237-248
Publication Year
2013
Authors
James Cook, Alex Fabrikant, Avinatan Hassidim
BibTeX
Abstract
We consider the algorithmic challenges behind a novel interface that simplifies
consumer research of online reviews by surfacing relevant comparable review
bundles: reviews for two or more of the items being researched, all generated in
similar enough circumstances to provide for easy comparison. This can be reviews by
the same reviewer, or by the same demographic category of reviewer, or reviews
focusing on the same aspect of the items. But such an interface will work only if
the review ecosystem often has comparable review bundles for common research tasks.
Here, we develop and evaluate practical algorithms for suggesting additional review
targets to reviewers to maximize comparable pair coverage, the fraction of
co-researched pairs of items that have both been reviewed by the same reviewer (or
more generally are comparable in one of several ways). We show the exact problem
and many subcases to be intractable, and give a greedy online, linear-time
2-approximation for a very general setting, and an offline 1.583-approximation for
a narrower setting. We evaluate the algorithms on the Google+ Local reviews
dataset, yielding more than 10x gain in pair coverage from six months of simulated
replacement of existing reviews by suggested reviews. Even allowing for 90% of
reviewers ignoring the suggestions, the pair coverage grows more than 2x in the
simulation. To explore other parts of the parameter space, we also evaluate the
algorithms on synthetic models.
