Learning from contextual information of geo-tagged web photos to rank personalized tourism attractions

This paper proposed a method that fully exploits contextual information of geo-tagged web photos to recommend tourism attractions to a user according to his personal interest and current time and location. The proposed method first detects tourism attractions from geo-tags, and estimates their popularity with users' photo quantity. Photos' taken time is used to discover temporal fluctuation of attractions' popularity and distance of consecutive photos is exploited to model the spatial influence to user's travel behavior. Photos' textual and visual information are used to reveal users' personal interests. Collaborative filtering is also adopted in the recommendation process. With all these contextual information, our method predicts a user's preference to a certain attraction from different aspects, and automatically combines the prediction scores to give the final recommendation result with a learning to rank model. Experiments on Panoramio dataset show that our method performs better than the state-of-the-art method, especially for users with little traveling history.

[1]  Antonio Torralba,et al.  Modeling the Shape of the Scene: A Holistic Representation of the Spatial Envelope , 2001, International Journal of Computer Vision.

[2]  Tat-Seng Chua,et al.  Research and applications on georeferenced multimedia: a survey , 2010, Multimedia Tools and Applications.

[3]  Matthijs C. Dorst.  Distinctive Image Features from Scale-Invariant Keypoints , 2011 .

[4]  Ramesh C. Jain,et al.  Image annotation by kNN-sparse graph-based label propagation over noisily tagged web images , 2011, TIST.

[5]  Marcel J. T. Reinders,et al.  Using flickr geotags to predict user travel behaviour , 2010, SIGIR.

[6]  Tat-Seng Chua,et al.  ViewFocus: explore places of interests on Google maps using photos with view direction filtering , 2009, MM '09.

[7]  TorralbaAntonio,et al.  Modeling the Shape of the Scene , 2001 .

[8]  Thorsten Joachims,et al.  Optimizing search engines using clickthrough data , 2002, KDD.

[9]  Touradj Ebrahimi,et al.  Geotag propagation in social networks based on user trust model , 2010, Multimedia Tools and Applications.

[10]  Yue Gao,et al.  W2Go: a travel guidance system by automatic landmark ranking , 2010, ACM Multimedia.

[11]  Jianyong Wang,et al.  Mining sequential patterns by pattern-growth: the PrefixSpan approach , 2004, IEEE Transactions on Knowledge and Data Engineering.

[12]  Dorin Comaniciu,et al.  Mean Shift: A Robust Approach Toward Feature Space Analysis , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[13]  Shuicheng Yan,et al.  Inferring semantic concepts from community-contributed images and noisy tags , 2009, ACM Multimedia.

[14]  Andrea Vedaldi,et al.  Vlfeat: an open and portable library of computer vision algorithms , 2010, ACM Multimedia.

[15]  Takahiro Hara,et al.  Mining people's trips from large scale geo-tagged photos , 2010, ACM Multimedia.

[16]  ChuaTat-Seng,et al.  Research and applications on georeferenced multimedia , 2011 .

[17]  Changhu Wang,et al.  Photo2Trip: generating travel routes from geo-tagged photos for trip planning , 2010, ACM Multimedia.

[18]  David Nistér,et al.  Scalable Recognition with a Vocabulary Tree , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

[19]  Mao Ye,et al.  Exploiting geographical influence for collaborative point-of-interest recommendation , 2011, SIGIR.

[20]  Jiebo Luo,et al.  A WORLDWIDE TOURISM RECOMMENDATION SYSTEM BASED ON GEOTAGGED WEB PHOTOS , 2010 .

[21]  Qi Tian,et al.  Mining flickr landmarks by modeling reconstruction sparsity , 2011, TOMCCAP.

[22]  Xing Xie,et al.  Mining city landmarks from blogs by graph modeling , 2009, ACM Multimedia.

[23]  Nenghai Yu,et al.  Flickr distance , 2008, ACM Multimedia.

[24]  Jitendra Malik,et al.  Normalized cuts and image segmentation , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[25]  Jiebo Luo,et al.  Aworldwide tourism recommendation system based on geotaggedweb photos , 2010, 2010 IEEE International Conference on Acoustics, Speech and Signal Processing.

[26]  Gerard Salton,et al.  A vector space model for automatic indexing , 1975, CACM.

[27]  Lars Backstrom,et al.  Find me if you can: improving geographical prediction with social and spatial proximity , 2010, WWW '10.

[28]  Jiebo Luo,et al.  Enhancing semantic and geographic annotation of web images via logistic canonical correlation regression , 2009, ACM Multimedia.

[29]  Steven M. Seitz,et al.  Photo tourism: exploring photo collections in 3D , 2006, ACM Trans. Graph..

[30]  Alexei A. Efros,et al.  IM2GPS: estimating geographic information from a single image , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[31]  Richard Szeliski,et al.  Modeling the World from Internet Photo Collections , 2008, International Journal of Computer Vision.

[32]  Jiebo Luo,et al.  Geotagging in multimedia and computer vision—a survey , 2010, Multimedia Tools and Applications.

[33]  Jon M. Kleinberg,et al.  Mapping the world's photos , 2009, WWW '09.

[34]  DayalUmeshwar,et al.  Mining Sequential Patterns by Pattern-Growth , 2004 .

[35]  Nenghai Yu,et al.  Photo2Trip: an interactive trip planning system based on geo-tagged photos , 2010, ACM Multimedia.

[36]  Ramesh C. Jain,et al.  One person labels one million images , 2010, ACM Multimedia.

[37]  Tomoharu Iwata,et al.  Travel route recommendation using geotags in photo sharing sites , 2010, CIKM.

[38]  Tat-Seng Chua,et al.  Image Annotation by Graph-Based Inference With Integrated Multiple/Single Instance Representations , 2010, IEEE Transactions on Multimedia.