# Design Patterns for Fusion-Based Object Retrieval

arXiv: https://arxiv.org/abs/1708.08715 · HTML: https://mlsanity.com/paper/1708.08715/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Aug 2017.

## Built on by (most cited first, 3 of 3)

- [Retrieval-Augmented Conversational Recommendation with Prompt-based Semi-Structured Natural Language State Tracking](https://mlsanity.com/paper/2406.00033/) (2024)
- [Self-Supervised Contrastive BERT Fine-tuning for Fusion-based Reviewed-Item Retrieval](https://mlsanity.com/paper/2308.00762/) (2023)
- [Semantic Answer Type Prediction using BERT: IAI at the ISWC SMART Task 2020](https://mlsanity.com/paper/2109.06714/) (2021)

## Abstract (arXiv)

We address the task of ranking objects (such as people, blogs, or verticals) that, unlike documents, do not have direct term-based representations. To be able to match them against keyword queries, evidence needs to be amassed from documents that are associated with the given object. We present two design patterns, i.e., general reusable retrieval strategies, which are able to encompass most existing approaches from the past. One strategy combines evidence on the term level (early fusion), while the other does it on the document level (late fusion). We demonstrate the generality of these patterns by applying them to three different object retrieval tasks: expert finding, blog distillation, and vertical ranking.