| Human-Computer Interaction and Pervasive Computing |
|
|
|
|
| CrowdTravel: leveraging heterogeneous crowdsourced data for scenic spot profiling |
| GUO Tong, GUO Bin, ZHANG Jia-fan, YU Zhi-wen, ZHOU Xing-she |
| School of Computer Science, Northwestern Polytechnical University, Xi’an 710129, China |
|
|
|
Abstract
A multi-aspect tourism information perception method based on multi-source social media data fusion was proposed in order to improve the efficiency of travel knowledge acquisition and provide effective travel assistance for users. A cross-media multi-aspect correlation approach was proposed to connect fragmented travel information after a preprocessing step for the heterogeneous travel related data, which resorted to the “scene-feature characterization” in order to make a multi-aspect characterization. The perception method mined typical travel route from historical travelogues based on the sequential pattern mining, which can be regarded as the recommended route. Connecting cross-media information was based on the similarity between the reviews and the image contexts. Results of experiments over a dataset of eight domestic popular scenic spots, which was collected from Dazhongdianping and Mafengwo, indicate that the approach makes a fine-grained characterization for the scenic spots and the provided travel route can meet different users’ needs．
|
|
Published: 25 April 2017
|
|
|
|
Cite this article:
GUO Tong, GUO Bin, ZHANG Jia-fan, YU Zhi-wen, ZHOU Xing-she. CrowdTravel: leveraging heterogeneous crowdsourced data for scenic spot profiling. JOURNAL OF ZHEJIANG UNIVERSITY (ENGINEERING SCIENCE), 2017, 51(4): 663-668.
|
多源社交数据融合的多角度旅游信息感知
为了提高用户获取旅游知识的效率,同时提供有效的旅行辅助,提出多源社交数据融合的多角度旅游信息感知方法.对异构的旅游相关数据进行预处理,提出跨媒体多角度关联方法来连接碎片化旅游信息,利用“景观-特征刻画”实现景点的多角度刻画.通过序列模式挖掘算法,从历史游记数据中得到典型旅游路线并向用户进行推荐.基于评论文本和图像上、下文的相似性,将图像和文本结合,实现跨媒体信息关联.从大众点评和蚂蜂窝上,采集国内8个热门景点数据进行实验.结果表明,采用该方法能够更细粒度地刻画景点,且提供的旅游路线能够满足不同用户的需求.
|
|
| ［1］ ZHANG D, GUO B, YU Z. The emergence of social and community intelligence ［J］. Computer, 2011,44(7): 21-28．
［2］ GUO B, WANG Z, YU Z, et al. Mobile crowd sensing and computing: the review of an emerging human-powered sensing paradigm ［J］. ACM Computing Surveys, 2015, 48(1): 1-31．
［3］ BIAN J, YANG Y, CHUA T S. Multimedia summarization for trending topics in microblogs ［C］ ∥ ACM International Conference on Conference on Information and Knowledge Management.San Francisco: ACM, 2013: 1807-1812．
［4］ LIU B, HU M, CHENG J. Opinion observer: analyzing and comparing opinions on the Web ［C］ ∥ International Conference on World Wide Web. Chiba: ACM, 2005: 342-351．
［5］ GONG Y, LIU X. Generic text summarization using relevance measure and latent semantic analysis ［C］ ∥ SIGIR ′01: Proceedings of the International ACM SIGIR Conference on Research and Development in Information Retrieval. New Orleans: ACM, 2001: 19-25．
［6］ RATTENBURY T, NAAMAN M. Methods for extracting place semantics from Flickr tags ［J］. ACM Transactions on the Web, 2009, 3(1): 1139-1141．
［7］ HAO Q, CAI R, WANG C, et al. Equip tourists with knowledge mined from travelogues ［C］ ∥ International Conference on World Wide Web. Raleigh: DBLP, 2010: 401-410．
［8］ SHEN D, SUN J T, LI H, et al. Document summarization using conditional random fields ［C］ ∥Proceedings of the International Joint Conference on Artificial Intelligence. Hyderabad: DBLP, 2007: 2862-2867．
［9］ WANG T, BAI C. Understand the city better: multimodal aspect-opinion summarization for travel ［C］ ∥ Web Information Systems EngineeringWISE. Thessaloniki, Greece: Springer， 2014．
［10］ GUO T, GUO B, ZHANG J, et al. CrowdTravel: Leveraging Heterogeneous Crowdsourced Data for Scenic Spot Profiling and Recommendation ［M］∥ Advances in Multimedia Information Processing-PCM 2016. Xi’an, China: Springer, 2016: 617-628．
［11］ LIN C Y, HOVY E. From single to multi-document summarization: a prototype system and its evaluation ［C］ ∥ Meeting on Association for Computational Linguistics. Philadelphia: ACM, 2002: 457-464．
［12］ WAN X, YANG J. Multi-document summarizationusing cluster-based link analysis ［C］ ∥ International ACM SIGIR Conference on Research and Development in Information Retrieval. Singapore: ACM, 2008:299-306．
［13］ LI L, ZHOU K, XUE G R, et al. Enhancing diversity, coverage and balance for summarization through structure learning ［C］ ∥ International Conference on World Wide Web. Madrid: DBLP, 2009: 71-80．
［14］ RADEV D R, JING H, STYS M, et al. Centroid-based summarization of multiple documents ［J］. Information Processing and Management, 2004, 40(6):919-938．
［15］ MOGHADDAM S, ESTER M. Opinion digger: an unsupervised opinion miner from unstructured product reviews ［C］ ∥ ACM Conference on Information and Knowledge Management. Toronto: ACM, 2010:1825-1828．
［16］ GUO H, ZHU H, GUO Z, et al. Product feature categorization with multilevel latent semantic association ［C］ ∥ ACM Conference on Information and Knowledge Management. Hong Kong, China: ACM, 2009: 1087-1096．
［17］ HAGHIGHI A, VANDERWENDE L. Exploring content models for multi-document summarization ［C］ ∥ Human Language Technologies: Conference of the North American Chapter of the Association of Computational Linguistics. Boulder: DBLP, 2009: 362-370．
［18］ FREY B J, DUECK D. Clustering by passing messages between data points ［J］. Science, 2007, 315(5814): 972-976．
［19］ TORRALBA A, MURPHY K P, FREEMAN W T, et al. Context-based vision system for place and object recognition ［C］ ∥ IEEE International Conference on Computer Vision. Beijing, China: IEEE, 2003: 273． |
|
Viewed |
|
|
|
Full text
|
|
|
|
|
Abstract
|
|
|
|
|
Cited |
|
|
|
|
| |
Shared |
|
|
|
|
| |
Discussed |
|
|
|
|
