Unsupervised Submodular Rank Aggregation on Score-based Permutations
This work addresses a practical need for combining rankings in applications like search and social media, but it appears incremental as it builds on existing submodular methods.
The paper tackled the problem of unsupervised rank aggregation on score-based permutations by proposing a submodular optimization approach using Lovasz Bregman divergence, achieving effective results across multiple tasks such as Information Retrieval and Social Networks.
Unsupervised rank aggregation on score-based permutations, which is widely used in many applications, has not been deeply explored yet. This work studies the use of submodular optimization for rank aggregation on score-based permutations in an unsupervised way. Specifically, we propose an unsupervised approach based on the Lovasz Bregman divergence for setting up linear structured convex and nested structured concave objective functions. In addition, stochastic optimization methods are applied in the training process and efficient algorithms for inference can be guaranteed. The experimental results from Information Retrieval, Combining Distributed Neural Networks, Influencers in Social Networks, and Distributed Automatic Speech Recognition tasks demonstrate the effectiveness of the proposed methods.