Exposing Query Identification for Search Transparency
- URL: http://arxiv.org/abs/2110.07701v1
- Date: Thu, 14 Oct 2021 20:19:27 GMT
- Title: Exposing Query Identification for Search Transparency
- Authors: Ruohan Li, Jianxiang Li, Bhaskar Mitra, Fernando Diaz, Asia J. Biega
- Abstract summary: We explore the feasibility of approximate exposing query identification (EQI) as a retrieval task by reversing the role of queries and documents in two classes of search systems.
We derive an evaluation metric to measure the quality of a ranking of exposing queries, as well as conducting an empirical analysis focusing on various practical aspects of approximate EQI.
- Score: 69.06545074617685
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Search systems control the exposure of ranked content to searchers. In many
cases, creators value not only the exposure of their content but, moreover, an
understanding of the specific searches where the content is surfaced. The
problem of identifying which queries expose a given piece of content in the
ranking results is an important and relatively under-explored search
transparency challenge. Exposing queries are useful for quantifying various
issues of search bias, privacy, data protection, security, and search engine
optimization.
Exact identification of exposing queries in a given system is computationally
expensive, especially in dynamic contexts such as web search. In quest of a
more lightweight solution, we explore the feasibility of approximate exposing
query identification (EQI) as a retrieval task by reversing the role of queries
and documents in two classes of search systems: dense dual-encoder models and
traditional BM25 models. We then propose how this approach can be improved
through metric learning over the retrieval embedding space. We further derive
an evaluation metric to measure the quality of a ranking of exposing queries,
as well as conducting an empirical analysis focusing on various practical
aspects of approximate EQI.
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