# POINTREC: A Test Collection for Narrative-driven Point of Interest Recommendation

arXiv: https://arxiv.org/abs/2105.09204 · HTML: https://mlsanity.com/paper/2105.09204/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 May 2021.

## Built on by (most cited first, 4 of 4)

- [Large Language Model Augmented Narrative Driven Recommendations](https://mlsanity.com/paper/2306.02250/) (2023)
- [DORIS-MAE: Scientific Document Retrieval using Multi-level Aspect-based Queries](https://mlsanity.com/paper/2310.04678/) (2023)
- [SafeGEO: Understanding Generative Engine Optimization Risks in Recommendation Agents](https://mlsanity.com/paper/2606.28356/) (2026)
- [How Data Scientists Review the Scholarly Literature](https://mlsanity.com/paper/2301.03774/) (2023)

## Abstract (arXiv)

This paper presents a test collection for contextual point of interest (POI) recommendation in a narrative-driven scenario. There, user history is not available, instead, user requests are described in natural language. The requests in our collection are manually collected from social sharing websites, and are annotated with various types of metadata, including location, categories, constraints, and example POIs. These requests are to be resolved from a dataset of POIs, which are collected from a popular online directory, and are further linked to a geographical knowledge base and enriched with relevant web snippets. Graded relevance assessments are collected using crowdsourcing, by pooling both manual and automatic recommendations, where the latter serve as baselines for future performance comparison. This resource supports the development of novel approaches for end-to-end POI recommendation as well as for specific semantic annotation tasks on natural language requests.