Query Completion Using Bandits for Engines Aggregation
release_n4skx7ld2zcxlpxmv6vg3mddry
by
Audrey Durand, Jean-Alexandre Beaumont, Christian Gagne, Michel Lemay,
Sebastien Paquet
2017
Abstract
Assisting users by suggesting completed queries as they type is a common
feature of search systems known as query auto-completion. A query
auto-completion engine may use prior signals and available information (e.g.,
user is anonymous, user has a history, user visited the site before the search
or not, etc.) in order to improve its recommendations. There are many possible
strategies for query auto-completion and a challenge is to design one optimal
engine that considers and uses all available information. When different
strategies are used to produce the suggestions, it becomes hard to rank these
heterogeneous suggestions. An alternative strategy could be to aggregate
several engines in order to enhance the diversity of recommendations by
combining the capacity of each engine to digest available information
differently, while keeping the simplicity of each engine. The main objective of
this research is therefore to find such mixture of query completion engines
that would beat any engine taken alone. We tackle this problem under the
bandits setting and evaluate four strategies to overcome this challenge.
Experiments conducted on three real datasets show that a mixture of engines can
outperform a single engine.
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