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The vast increase in amount and complexity of digital content led to a wideinterest in ad-hoc retrieval systems in recent years. Complementary, theexistence of heterogeneous data sources and retrieval models stimulated theproliferation of increasingly ingenious and effective rank aggregationfunctions. Although recently proposed rank aggregation functions are promisingwith respect to effectiveness, existing proposals in the area usually overlookefficiency aspects. We propose an innovative rank aggregation function that isunsupervised, intrinsically multimodal, and targeted for fast retrieval and topeffectiveness performance. We introduce the concepts of embedding and indexingof graph-based rank-aggregation representation models, and their applicationfor search tasks. Embedding formulations are also proposed for graph-based rankrepresentations. We introduce the concept of fusion vectors, a late-fusionrepresentation of objects based on ranks, from which an intrinsicallyrank-aggregation retrieval model is defined. Next, we present an approach forfast retrieval based on fusion vectors, thus promoting an efficient rankaggregation system. Our method presents top effectiveness performance amongstate-of-the-art related work, while bringing novel aspects of multimodalityand effectiveness. Consistent speedups are achieved against the recentbaselines in all datasets considered.

Fusion vectors: Embedding Graph Fusions for Efficient Unsupervised Rank Aggregation