Diversity driven Query Rewriting in Search Advertising
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by
Akash Kumar Mohankumar, Nikit Begwani, Amit Singh
2021
Abstract
Retrieving keywords (bidwords) with the same intent as query, referred to as
close variant keywords, is of prime importance for effective targeted search
advertising. For head and torso search queries, sponsored search engines use a
huge repository of same intent queries and keywords, mined ahead of time.
Online, this repository is used to rewrite the query and then lookup the
rewrite in a repository of bid keywords contributing to significant revenue.
Recently generative retrieval models have been shown to be effective at the
task of generating such query rewrites. We observe two main limitations of such
generative models. First, rewrites generated by these models exhibit low
lexical diversity, and hence the rewrites fail to retrieve relevant keywords
that have diverse linguistic variations. Second, there is a misalignment
between the training objective - the likelihood of training data, v/s what we
desire - improved quality and coverage of rewrites. In this work, we introduce
CLOVER, a framework to generate both high-quality and diverse rewrites by
optimizing for human assessment of rewrite quality using our diversity-driven
reinforcement learning algorithm. We use an evaluation model, trained to
predict human judgments, as the reward function to finetune the generation
policy. We empirically show the effectiveness of our proposed approach through
offline experiments on search queries across geographies spanning three major
languages. We also perform online A/B experiments on Bing, a large commercial
search engine, which shows (i) better user engagement with an average increase
in clicks by 12.83% accompanied with an average defect reduction by 13.97%, and
(ii) improved revenue by 21.29%.
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