Fusion vectors: Embedding Graph Fusions for Efficient Unsupervised Rank Aggregation
This work addresses efficiency issues in rank aggregation for ad-hoc retrieval systems, offering a novel approach that improves speed while maintaining high effectiveness, though it is incremental in nature.
The paper tackles the problem of inefficient rank aggregation in retrieval systems by proposing an unsupervised, multimodal method called fusion vectors, which achieves top effectiveness performance and consistent speedups against recent baselines across all datasets.
The vast increase in amount and complexity of digital content led to a wide interest in ad-hoc retrieval systems in recent years. Complementary, the existence of heterogeneous data sources and retrieval models stimulated the proliferation of increasingly ingenious and effective rank aggregation functions. Although recently proposed rank aggregation functions are promising with respect to effectiveness, existing proposals in the area usually overlook efficiency aspects. We propose an innovative rank aggregation function that is unsupervised, intrinsically multimodal, and targeted for fast retrieval and top effectiveness performance. We introduce the concepts of embedding and indexing of graph-based rank-aggregation representation models, and their application for search tasks. Embedding formulations are also proposed for graph-based rank representations. We introduce the concept of fusion vectors, a late-fusion representation of objects based on ranks, from which an intrinsically rank-aggregation retrieval model is defined. Next, we present an approach for fast retrieval based on fusion vectors, thus promoting an efficient rank aggregation system. Our method presents top effectiveness performance among state-of-the-art related work, while bringing novel aspects of multimodality and effectiveness. Consistent speedups are achieved against the recent baselines in all datasets considered.