Cheap IR Evaluation: Fewer Topics, No Relevance Judgements, and Crowdsourced Assessments

TL;DR

This paper proposes a resource-efficient, crowdsourced approach to IR evaluation that reduces reliance on extensive relevance judgments and fewer topics, aiming to improve test collection methodologies.

Contribution

It introduces a novel, more principled method for IR effectiveness evaluation that minimizes resource use and leverages crowdsourcing, advancing current evaluation practices.

Findings

Effective IR evaluation with fewer topics

Crowdsourced relevance assessments are viable

Resource savings without compromising accuracy

Abstract

To evaluate Information Retrieval (IR) effectiveness, a possible approach is to use test collections, which are composed of a collection of documents, a set of description of information needs (called topics), and a set of relevant documents to each topic. Test collections are modelled in a competition scenario: for example, in the well known TREC initiative, participants run their own retrieval systems over a set of topics and they provide a ranked list of retrieved documents; some of the retrieved documents (usually the first ranked) constitute the so called pool, and their relevance is evaluated by human assessors; the document list is then used to compute effectiveness metrics and rank the participant systems. Private Web Search companies also run their in-house evaluation exercises; although the details are mostly unknown, and the aims are somehow different, the overall approach shares several issues with the test collection approach. The aim of this work is to: (i) develop and improve some state-of-the-art work on the evaluation of IR effectiveness while saving resources, and (ii) propose a novel, more principled and engineered, overall approach to test collection based effectiveness evaluation. [...]