  Entity Resolution, also called record linkage or deduplication, refers to the
process of identifying and merging duplicate versions of the same entity into a
unified representation. The standard practice is to use a Rule based or Machine
Learning based model that compares entity pairs and assigns a score to
represent the pairs' Match/Non-Match status. However, performing an exhaustive
pair-wise comparison on all pairs of records leads to quadratic matcher
complexity and hence a Blocking step is performed before the Matching to group
similar entities into smaller blocks that the matcher can then examine
exhaustively. Several blocking schemes have been developed to efficiently and
effectively block the input dataset into manageable groups. At CareerBuilder
(CB), we perform deduplication on massive datasets of people profiles collected
from disparate sources with varying informational content. We observed that,
employing a single blocking technique did not cover the base for all possible
scenarios due to the multi-faceted nature of our data sources. In this paper,
we describe our ensemble approach to blocking that combines two different
blocking techniques to leverage their respective strengths.
