POINTREC: A Test Collection for Narrative-driven Point of Interest Recommendation
This provides a resource for researchers working on narrative-driven POI recommendation, but it is incremental as it focuses on creating a dataset rather than proposing a novel method.
The paper introduces a test collection for point of interest recommendation where user requests are in natural language without history, and it includes manually collected requests, enriched POI data, and crowdsourced relevance assessments to support development of new methods.
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.