  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.
