Crowd-enabled Pareto-Optimal Objects Finding Employing Multi-Pairwise-Comparison Questions

摘要: Today, Pareto-optimal objects finding has been applied in various fields, such as group decision making and opinion collection. Many of the existing solutions to this problem require explicit attributes for objects. However, these cannot be obtained sometimes. To address issue, we propose an algorithm, which uses preference relations given by crowdsourcing, find with shorter latency lower monetary costs. It employs two multi-pairwise-comparison question models: BEST-form BETTER-form questions. Multiple BEST (or BETTER) questions can sent crowds concurrently. Extensive experimental results show that number reduces greatly. In addition, numerical is significantly shortened at a reasonable cost, compared methods.