IRApr 18, 2021

Transitivity, Time Consumption, and Quality of Preference Judgments in Crowdsourcing

arXiv:2104.08926v112 citations
Originality Synthesis-oriented
AI Analysis

This addresses the reliability of crowdsourced preference judgments for information retrieval, but it is incremental as it builds on prior work with trained judges.

The study investigated whether transitivity holds for strict and weak preference judgments collected via crowdsourcing, finding that only strict judgments are transitive, while weak judgments differ in transitivity, time consumption, and quality.

Preference judgments have been demonstrated as a better alternative to graded judgments to assess the relevance of documents relative to queries. Existing work has verified transitivity among preference judgments when collected from trained judges, which reduced the number of judgments dramatically. Moreover, strict preference judgments and weak preference judgments, where the latter additionally allow judges to state that two documents are equally relevant for a given query, are both widely used in literature. However, whether transitivity still holds when collected from crowdsourcing, i.e., whether the two kinds of preference judgments behave similarly remains unclear. In this work, we collect judgments from multiple judges using a crowdsourcing platform and aggregate them to compare the two kinds of preference judgments in terms of transitivity, time consumption, and quality. That is, we look into whether aggregated judgments are transitive, how long it takes judges to make them, and whether judges agree with each other and with judgments from TREC. Our key findings are that only strict preference judgments are transitive. Meanwhile, weak preference judgments behave differently in terms of transitivity, time consumption, as well as of quality of judgment.

Foundations

The foundational work for this paper's niche, ranked by how specifically the neighbourhood builds on it — not by global fame.

Your Notes