Deep Partial Rank Aggregation for Personalized Attributes

Authors

  • Qianqian Xu Institute of Computing Technology, CAS
  • Zhiyong Yang Institute of Information Engineering, CAS University of Chinese Academy of Sciences
  • Zuyao Chen University of Chinese Academy of Sciences
  • Yangbangyan Jiang Institute of Information Engineering, CAS University of Chinese Academy of Sciences
  • Xiaochun Cao Institute of Information Engineering, CAS University of Chinese Academy of Sciences Peng Cheng Laboratory
  • Yuan Yao Hong Kong University of Science and Technology
  • Qingming Huang Institute of Computing Technology, CAS University of Chinese Academy of Sciences Chinese Academy of Sciences Peng Cheng Laboratory

Keywords:

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Abstract

In this paper, we study the problem of how to aggregate pairwise personalized attributes (PA) annotations (e.g., Shoes A is more comfortable than B) from different annotators on the crowdsourcing platforms, which is an emerging topic gaining increasing attention in recent years. Given the crowdsourced annotations, the majority of the traditional literature assumes that all the pairs in the collected dataset are distinguishable. However, this assumption is incompatible with how humans perceive attributes since indistinguishable pairs are ubiquitous for the annotators due to the limitation of human perception. To attack this problem, we propose a novel deep prediction model that could simultaneously detect the indistinguishable pairs and aggregate ranking results for distinguishable pairs. First of all, we represent the pairwise annotations as a multi-graph. Based on such data structure, we propose an end-to-end partial ranking model which consists of a deep backbone architecture and a probabilistic model that captures the generative process of the partial rank annotations. Specifically, to recognize the indistinguishable pairs, the probabilistic model we proposed is equipped with an adaptive perception threshold, where indistinguishable pairs could be automatically detected when the absolute value of the score difference is below the learned threshold. In our empirical studies, we perform a series of experiments on three real-world datasets: LFW-10, Shoes, and Sun. The corresponding results consistently show the superiority of our proposed model.

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Published

2021-05-18

How to Cite

Xu, Q., Yang, Z., Chen, Z., Jiang, Y., Cao, X., Yao, Y., & Huang, Q. (2021). Deep Partial Rank Aggregation for Personalized Attributes. Proceedings of the AAAI Conference on Artificial Intelligence, 35(1), 678-688. Retrieved from https://ojs.aaai.org/index.php/AAAI/article/view/16148

Issue

Section

AAAI Technical Track on Application Domains