NDLI: Crowdsourcing Pareto-Optimal Object Finding By Pairwise Comparisons

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Crowdsourcing Pareto-Optimal Object Finding By Pairwise Comparisons

Content Provider	ACM Digital Library
Author	Zhang, Gensheng Li, Chengkai Zaruba, Gergely V. Asudeh, Abolfazl Hassan, Naeemul
Abstract	This is the first study of crowdsourcing Pareto-optimal object finding over partial orders and by pairwise comparisons, which has applications in public opinion collection, group decision making, and information exploration. Departing from prior studies on crowdsourcing skyline and ranking queries, it considers the case where objects do not have explicit attributes and preference relations on objects are strict partial orders. The partial orders are derived by aggregating crowdsourcers' responses to pairwise comparison questions. The goal is to find all Pareto-optimal objects by the fewest possible questions. It employs an iterative question-selection framework. Guided by the principle of eagerly identifying non-Pareto optimal objects, the framework only chooses candidate questions which must satisfy three conditions. This design is both sufficient and efficient, as it is proven to find a short terminal question sequence. The framework is further steered by two ideas---macro-ordering and micro-ordering. By different micro-ordering heuristics, the framework is instantiated into several algorithms with varying power in pruning questions. Experiment results using both real crowdsourcing marketplace and simulations exhibited not only orders of magnitude reductions in questions when compared with a brute-force approach, but also close-to-optimal performance from the most efficient instantiation.
Starting Page	753
Ending Page	762
Page Count	10
File Format	PDF
ISBN	9781450337946
DOI	10.1145/2806416.2806451
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2015-10-17
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Crowdsourcing Pareto-optimal object Human computation Pairwise comparison Decision making Opinion collection Information exploration Skyline query Partial order
Content Type	Text
Resource Type	Article

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