TY - GEN
T1 - Crowdtravel
T2 - 17th Pacific-Rim Conference on Multimedia, PCM 2016
AU - Guo, Tong
AU - Guo, Bin
AU - Zhang, Jiafan
AU - Yu, Zhiwen
AU - Zhou, Xingshe
N1 - Publisher Copyright:
© Springer International Publishing AG 2016.
PY - 2016
Y1 - 2016
N2 - With the prosperity of mobile social networks, more and more people are willing to share their travel experiences and feelings on the Web, which provides abundant knowledge for people who are going to make travel plans. Travel reviews and travelogues are two major ways of social travel sharing. They are complementary in terms of structure, content, and interaction, forming a sort of fragmented travel knowledge. Moreover, the ever-increasing reviews and travelogues may impose the burden on gaining and reorganizing knowledge while making travel plans. Over these issues, this paper proposes CrowdTravel, a multi-source social media data fusion approach for multi-aspect tourism information perception and intelligent recommendation, which can provide travelling assistance for users by crowd intelligence mining. First, we propose a cross-media multi-aspect correlation method to connect fragmented travel information. Second, we mine popular and personalized travel routes from travelogues and make intelligent recommendation based on sequential pattern mining. Finally, we achieve cross-media relevance information based on the similarity between the reviews and image contexts. We conduct experiments over a dataset of eight domestic popular scenic spots, which is collected from two popular social websites about travel, namely Dazhongdianping and Mafengwo. The results indicate that our approach attains fine-grained characterization for the scenic spots and the extracted travel routes can meet different users’ needs.
AB - With the prosperity of mobile social networks, more and more people are willing to share their travel experiences and feelings on the Web, which provides abundant knowledge for people who are going to make travel plans. Travel reviews and travelogues are two major ways of social travel sharing. They are complementary in terms of structure, content, and interaction, forming a sort of fragmented travel knowledge. Moreover, the ever-increasing reviews and travelogues may impose the burden on gaining and reorganizing knowledge while making travel plans. Over these issues, this paper proposes CrowdTravel, a multi-source social media data fusion approach for multi-aspect tourism information perception and intelligent recommendation, which can provide travelling assistance for users by crowd intelligence mining. First, we propose a cross-media multi-aspect correlation method to connect fragmented travel information. Second, we mine popular and personalized travel routes from travelogues and make intelligent recommendation based on sequential pattern mining. Finally, we achieve cross-media relevance information based on the similarity between the reviews and image contexts. We conduct experiments over a dataset of eight domestic popular scenic spots, which is collected from two popular social websites about travel, namely Dazhongdianping and Mafengwo. The results indicate that our approach attains fine-grained characterization for the scenic spots and the extracted travel routes can meet different users’ needs.
KW - Crowd intelligence
KW - Intelligent recommendation
KW - Multi-aspect characterization
KW - Scenic spot profiling
KW - Social media data fusion
UR - http://www.scopus.com/inward/record.url?scp=85006873062&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-48896-7_61
DO - 10.1007/978-3-319-48896-7_61
M3 - 会议稿件
AN - SCOPUS:85006873062
SN - 9783319488950
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 617
EP - 628
BT - Advances in Multimedia Information Processing – 17th Pacific-Rim Conference on Multimedia, PCM 2016, Proceedings
A2 - Chen, Enqing
A2 - Tie, Yun
A2 - Gong, Yihong
PB - Springer Verlag
Y2 - 15 September 2016 through 16 September 2016
ER -