论文部分内容阅读
The popularity of camera phones and photo sharing websites,e.g.Flickr and Panoramio,has led to huge volumes of community-contributed geotagged photos,which could be regarded as digital footprints of photo takers.Thus,mining geotagged photos for travel recommendation has become a hot topic.However,most existing work recommends travel locations based on the knowledge mined from photo logs (e.g.time,location),and largely ignores the knowledge implied in the photo contents.In this paper,we propose a geotagged photos mining based personalized gender-aware travel location recommendation approach,which considers both photo logs and photo contents.Firstly,it uses an entropy-based mobility measure to classify geotagged photos into tour photos or non-tour photos.Secondly,it conducts gender recognition based on face detection from tour photos.Thirdly,it builds the gender-aware profile of travel locations.Finally,it recommends personalized travel locations considering both user gender and similarity.Our approach is evaluated on a dataset,which contains geotagged photos taken in eleven cities of China.Experimental results show the effectiveness of the proposed approach in terms of prediction precision of travel behavior.