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

TL;DR

This paper introduces POINTREC, a comprehensive test collection for narrative-driven POI recommendation, enabling new research in natural language request resolution without user history data.

Contribution

It provides a novel dataset with natural language requests, metadata, and relevance assessments, facilitating research in end-to-end POI recommendation and semantic annotation.

Findings

Dataset includes manually collected requests with metadata.

Relevance assessments obtained via crowdsourcing.

Baseline recommendations established for future comparison.

Abstract

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.