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This paper presents a test collection for contextual point of interest (POI)recommendation in a narrative-driven scenario. There, user history is notavailable, instead, user requests are described in natural language. Therequests 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 resolvedfrom a dataset of POIs, which are collected from a popular online directory,and are further linked to a geographical knowledge base and enriched withrelevant web snippets. Graded relevance assessments are collected usingcrowdsourcing, by pooling both manual and automatic recommendations, where thelatter serve as baselines for future performance comparison. This resourcesupports the development of novel approaches for end-to-end POI recommendationas well as for specific semantic annotation tasks on natural language requests.

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