# Leveraging semantically similar queries for ranking via combining representations

arXiv: https://arxiv.org/abs/2106.12621 · HTML: https://mlsanity.com/paper/2106.12621/

## Key facts

- Cited by 2 papers in the map (data through 7 Oct 2026).
- Builds on 13 of its 36 references that are in the map.
- First posted on arXiv 23 Jun 2021.

## Builds on (most cited first, 13 of 13)

- [Inductive Representation Learning on Large Graphs](https://mlsanity.com/paper/1706.02216/) (2017)
- [UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction](https://mlsanity.com/paper/1802.03426/) (2018)
- [node2vec: Scalable Feature Learning for Networks](https://mlsanity.com/paper/1607.00653/) (2016)
- [Deep Visual Domain Adaptation: A Survey](https://mlsanity.com/paper/1802.03601/) (2018)
- [Learning from positive and unlabeled data: a survey](https://mlsanity.com/paper/1811.04820/) (2018)
- [Statistical inference on random dot product graphs: a survey](https://mlsanity.com/paper/1709.05454/) (2017)
- [VERSE: Versatile Graph Embeddings from Similarity Measures](https://mlsanity.com/paper/1803.04742/) (2018)
- [NetSMF: Large-Scale Network Embedding as Sparse Matrix Factorization](https://mlsanity.com/paper/1906.11156/) (2019)
- [GraSPy: Graph Statistics in Python](https://mlsanity.com/paper/1904.05329/) (2019)
- [Vertex nomination schemes for membership prediction](https://mlsanity.com/paper/1312.2638/) (2013)
- [Vertex Nomination, Consistent Estimation, and Adversarial Modification](https://mlsanity.com/paper/1905.01776/) (2019)
- [Vertex nomination: The canonical sampling and the extended spectral nomination schemes](https://mlsanity.com/paper/1802.04960/) (2018)
- [On consistent vertex nomination schemes](https://mlsanity.com/paper/1711.05610/) (2017)

## Built on by (most cited first, 2 of 2)

- [Approximately optimal domain adaptation with Fisher's Linear Discriminant](https://mlsanity.com/paper/2302.14186/) (2023)
- [Detection of Model-based Planted Pseudo-cliques in Random Dot Product Graphs by the Adjacency Spectral Embedding and the Graph Encoder Embedding](https://mlsanity.com/paper/2312.11054/) (2023)

## Abstract (arXiv)

In modern ranking problems, different and disparate representations of the items to be ranked are often available. It is sensible, then, to try to combine these representations to improve ranking. Indeed, learning to rank via combining representations is both principled and practical for learning a ranking function for a particular query. In extremely data-scarce settings, however, the amount of labeled data available for a particular query can lead to a highly variable and ineffective ranking function. One way to mitigate the effect of the small amount of data is to leverage information from semantically similar queries. Indeed, as we demonstrate in simulation settings and real data examples, when semantically similar queries are available it is possible to gainfully use them when ranking with respect to a particular query. We describe and explore this phenomenon in the context of the bias-variance trade off and apply it to the data-scarce settings of a Bing navigational graph and the Drosophila larva connectome.