Travel Recommendation by Mining People Attributes and Travel Group Types From Community-Contributed Photos

TLDR: The experiments confirm that people attributes of individuals and groups are promising and orthogonal to prior works using travel logs only and can further improve prior travel recommendation methods especially for difficult predictions by further leveraging user contexts via mobile devices.

Abstract: Leveraging community-contributed data (e.g., blogs, GPS logs, and geo-tagged photos) for personalized recommendation is one of the active research problems since there are rich contexts and human activities in such explosively growing data. In this work, we focus on personalized travel recommendation and show promising applications by leveraging the freely available community-contributed photos. We propose to conduct personalized travel recommendation by further considering specific user profiles or attributes (e.g., gender, age, race) as well as travel group types (e.g., family, friends, couple). Instead of mining photo logs only, we exploit the automatically detected people attributes and travel group types in the photo contents. By information-theoretic measures, we demonstrate that such detected user profiles are informative and effective for travel recommendation-especially providing a promising aspect for personalization. We effectively mine the demographics of individual and group travelers for different locations (or landmarks) and their travel paths. A probabilistic Bayesian learning framework which further entails mobile recommendation on the spot is introduced as well. We experiment on more than 10 million photos collected from 19 major cities worldwide and conduct the extensive investigation of profiling activities in communities according to temporal and spatial information. Note that the photos in the paper attribute to various Flickr users under the Creative Commons License. The experiments confirm that people attributes of individuals and groups are promising and orthogonal to prior works using travel logs only and can further improve prior travel recommendation methods especially for difficult predictions by further leveraging user contexts via mobile devices.