User Similarity based Gender-Aware Travel Location Recommendation by Mining Geotagged Photos
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.
Geotagged photos Travel location recommendation
Zhenxing Xu Ling Chen Haodong Guo Mingqi Lv Gencai Chen
College of Computer Science,Zhejiang University,Hangzhou 310027,P.R. China
国内会议
北京
英文
1-8
2014-09-13（万方平台首次上网日期，不代表论文的发表时间）