Mining Local Specialties for Travelers by Leveraging Structured and Unstructured Data
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 h...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2012-01-01
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| Series: | Advances in Multimedia |
| Online Access: | http://dx.doi.org/10.1155/2012/987124 |
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| _version_ | 1849406182665486336 |
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| author | Kai Jiang Like Liu Rong Xiao Nenghai Yu |
| author_facet | Kai Jiang Like Liu Rong Xiao Nenghai Yu |
| author_sort | Kai Jiang |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-4b6e9eb7efc846b79acce3dfce316e8d |
| institution | Kabale University |
| issn | 1687-5680 1687-5699 |
| language | English |
| publishDate | 2012-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advances in Multimedia |
| spelling | doaj-art-4b6e9eb7efc846b79acce3dfce316e8d2025-08-20T03:36:27ZengWileyAdvances in Multimedia1687-56801687-56992012-01-01201210.1155/2012/987124987124Mining Local Specialties for Travelers by Leveraging Structured and Unstructured DataKai Jiang0Like Liu1Rong Xiao2Nenghai Yu3MOE-Microsoft Key Laboratory of Multimedia Computing and Communication, University of Science and Technology of China, Anhui, Hefei 230027, ChinaMicrosoft Research Asia, Tower 2, No. 5 Dan Ling Street, Haidian District, Beijing 100080, ChinaMicrosoft Research Asia, Tower 2, No. 5 Dan Ling Street, Haidian District, Beijing 100080, ChinaMOE-Microsoft Key Laboratory of Multimedia Computing and Communication, University of Science and Technology of China, Anhui, Hefei 230027, ChinaRecently, 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.http://dx.doi.org/10.1155/2012/987124 |
| spellingShingle | Kai Jiang Like Liu Rong Xiao Nenghai Yu Mining Local Specialties for Travelers by Leveraging Structured and Unstructured Data Advances in Multimedia |
| title | Mining Local Specialties for Travelers by Leveraging Structured and Unstructured Data |
| title_full | Mining Local Specialties for Travelers by Leveraging Structured and Unstructured Data |
| title_fullStr | Mining Local Specialties for Travelers by Leveraging Structured and Unstructured Data |
| title_full_unstemmed | Mining Local Specialties for Travelers by Leveraging Structured and Unstructured Data |
| title_short | Mining Local Specialties for Travelers by Leveraging Structured and Unstructured Data |
| title_sort | mining local specialties for travelers by leveraging structured and unstructured data |
| url | http://dx.doi.org/10.1155/2012/987124 |
| work_keys_str_mv | AT kaijiang mininglocalspecialtiesfortravelersbyleveragingstructuredandunstructureddata AT likeliu mininglocalspecialtiesfortravelersbyleveragingstructuredandunstructureddata AT rongxiao mininglocalspecialtiesfortravelersbyleveragingstructuredandunstructureddata AT nenghaiyu mininglocalspecialtiesfortravelersbyleveragingstructuredandunstructureddata |