All Title Author
Keywords Abstract

Mining Local Specialties for Travelers by Leveraging Structured and Unstructured Data

DOI: 10.1155/2012/987124

Full-Text   Cite this paper   Add to My Lib


Recently, many local review websites such as Yelp are emerging, which have greatly facilitated people's daily life such as cuisine hunting. However they failed to meet travelers' demands because travelers are more concerned about a city's local specialties instead of the city's high ranked restaurants. To solve this problem, this paper presents a local specialty mining algorithm, which utilizes both the structured data from local review websites and the unstructured user-generated content (UGC) from community Q&A websites, and travelogues. The proposed algorithm extracts dish names from local review data to build a document for each city, and applies tfidf weighting algorithm on these documents to rank dishes. Dish-city correlations are calculated from unstructured UGC, and combined with the tfidf ranking score to discover local specialties. Finally, duplicates in the local specialty mining results are merged. A recommendation service is built to present local specialties to travelers, along with specialties' associated restaurants, Q&A threads, and travelogues. Experiments on a large data set show that the proposed algorithm can achieve a good performance, and compared to using local review data alone, leveraging unstructured UGC can boost the mining performance a lot, especially in large cities. 1. Introduction The notion of SoLoMo (social local mobile) has induced an explosion of mobile technologies and applications. Under this trend, many local review social network services such as Yelp [1], Dianping [2], and Baidu Shenbian [3] are emerging. These websites enable users to explore, search, share and review local business entities, and indeed provide valuable information for people’s daily life. Take cuisine hunting for instance, these applications may provide a great answer to the question “What are the fabulous restaurants nearby and what are the featured dishes in these restaurants?” That might satisfy local residents, but for a traveler, that’s not enough. What makes travelers different from local residents is that instead of nearby restaurant and their featured dishes, a traveler is more concerned about the local specialties of the city. A local specialty means a dish is so special in some way that it seldom found in other cities. It may be the ingredients, flavor or cooking style that makes the dish special, and the local specialty often reveals the local culture and lifestyle. Thus, to experience local specialties is always an important task for travelers. Unfortunately, current local review services cannot meet travelers’ demands well,


[1]  Yelp,
[2]  Dianping,
[3]  Baidu Shenbian,
[4]  Yahoo! Answers,
[5]  Y. Gao, J. Tang, R. Hong, Q. Dai, T. S. Chua, and R. Jain, “W2Go: a travel guidance system by automatic landmark ranking,” in Proceedings of the 18th ACM International Conference on Multimedia ACM Multimedia, pp. 123–132, October 2010.
[6]  Yahoo! Travel,
[7]  R. Ji, X. Xie, H. Yao, and W. Y. Ma, “Mining city landmarks from blogs by graph modeling,” in Proceedings of the 17th ACM International Conference on Multimedia, MM'09, with Co-located Workshops and Symposiums, pp. 105–114, October 2009.
[8]  Windows Live Space,
[9]  M. Ye, R. Xiao, W. C. Lee, and X. Xie, “On theme location discovery for travelogue services,” in Proceedings of the 34th international ACM SIGIR conference on Research and development in Information, pp. 465–474, 2011.
[10]  Q. Hao, R. Cai, J. M. Yang et al., “TravelScope: standing on the shoulders of dedicated travelers,” in Proceedings of the 17th ACM International Conference on Multimedia, MM'09, with Co-located Workshops and Symposiums, pp. 1021–1022, October 2009.
[11]  Q. Hao, R. Cai, C. Wang et al., “Equip tourists with knowledge mined from travelogues,” in Proceedings of the 19th International World Wide Web Conference (WWW '10), pp. 401–410, April 2010.
[12]  Q. Hao, R. Cai, X. J. Wang, J. M. Yang, Y. Pang, and L. Zhang, “Generating location overviews with images and tags by mining user-generated travelogues,” in Proceedings of the 17th ACM International Conference on Multimedia, MM'09, with Co-located Workshops and Symposiums, pp. 801–804, October 2009.
[13]  T. C. Peng and C. C. Shih, “Mining Chinese restaurant reviews for cuisine name extraction: an application to cuisine guide service,” in Proceedings of the International Conference on Information Engineering and Computer Science (ICIECS '09), pp. 1–4, December 2009.
[14]  C. C. Shih, T. C. Peng, and W. S. Lai, “Mining the blogosphere to generate local cuisine hotspots for mobile map service,” in Proceedings of the 4th International Conference on Digital Information Management (ICDIM '09), pp. 151–158, November 2009.
[15]  G. Salton, E. A. Fox, and H. Wu, “Extended Boolean information retrieval,” Communications of the ACM, vol. 26, no. 11, pp. 1022–1036, 1983.
[16]  Quora,
[17]  Douguo,
[18]  J. M. Kleinberg, “Authoritative sources in a hyperlinked environment,” Journal of the ACM, vol. 46, no. 5, pp. 604–632, 1999.
[19]  Baidu Zhidao,
[20]  Soso Wenwen,
[21]  Sina iAsk,
[22]  Sina travel blog,
[23]  Netease travel blog,
[24]  Lvping,
[25]  C. D. Manning, P. Raghavan, and H. Schutze, Introduction to Information Retrieval, Cambridge University Press, Cambridge, UK, 2008.


comments powered by Disqus