  Similarity join, which can find similar objects (e.g., products, names,
addresses) across different sources, is powerful in dealing with variety in big
data, especially web data. Threshold-driven similarity join, which has been
extensively studied in the past, assumes that a user is able to specify a
similarity threshold, and then focuses on how to efficiently return the object
pairs whose similarities pass the threshold. We argue that the assumption about
a well set similarity threshold may not be valid for two reasons. The optimal
thresholds for different similarity join tasks may vary a lot. Moreover, the
end-to-end time spent on similarity join is likely to be dominated by a
back-and-forth threshold-tuning process.
  In response, we propose preference-driven similarity join. The key idea is to
provide several result-set preferences, rather than a range of thresholds, for
a user to choose from. Intuitively, a result-set preference can be considered
as an objective function to capture a user's preference on a similarity join
result. Once a preference is chosen, we automatically compute the similarity
join result optimizing the preference objective. As the proof of concept, we
devise two useful preferences and propose a novel preference-driven similarity
join framework coupled with effective optimization techniques. Our approaches
are evaluated on four real-world web datasets from a diverse range of
application scenarios. The experiments show that preference-driven similarity
join can achieve high-quality results without a tedious threshold-tuning
process.
