Name
Abstract | ||
|---|---|---|
E-Commerce has shown its exponentially-growing business value in the past decade. However, in contrast to the successful examples in online sales, such as Amazon1 and eBay2, the online barter business is still underexplored due to the lack of corresponding information aggregation service. In this paper, we design and implement a novel vertical search engine, called ExSearch, to aggregate online barter information for developing the barter market. Different from classical general purpose Web search engines, ExSearch adopts a focused crawler to gather related information from various websites. We propose to automatically extract the barter information from free-text Web pages such that the unstructured information is represented in structured databases. In addition, we utilize the data mining techniques such as regression to fulfill the missing information, which cannot be extracted from the Web pages. Finally, we validate and rank the search results according to user queries. Experimental results show that each component module in our proposed ExSearch system is efficient and effective. The volunteer users are satisfied by and interested in this novel vertical search engine. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1145/1645953.1646125 | CIKM |
Keywords | Field | DocType |
missing information,barter market,online barter business,barter information,aggregate online barter information,search engine,novel vertical search engine,corresponding information aggregation service,search result,unstructured information,information extraction,e commerce,vertical,ranking,barter,business value,web pages,machine learning,web search engine,satisfiability,focused crawler,data mining | Web search engine,Data mining,Vertical search,World Wide Web,Business value,Search engine,Web page,Information retrieval,Computer science,Barter,Information extraction,Focused crawler | Conference |
Citations | PageRank | References |
3 | 0.37 | 24 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Lei Ji | 1 | 59 | 4.49 |
| Jun Yan | 2 | 1798 | 85.25 |
| Ning Liu | 3 | 253 | 15.62 |
| Wen Zhang | 4 | 137 | 7.15 |
| Weiguo Fan | 5 | 2055 | 133.38 |
| Zheng Chen | 6 | 5019 | 256.89 |