Name
Abstract | ||
|---|---|---|
Commodity information such as prices and public reviews is always the concern of consumers. Helping them conveniently acquire these information as an instant reference is often of practical significance for their purchase activities. Nowadays, Web 2.0, linked data clouds, and the pervasiveness of smart hand held devices have created opportunities for this demand, i.e., users could just snap a photo of any commodity that is of interest at anytime and anywhere, and retrieve the relevant information via their Internet-linked mobile devices. Nonetheless, compared with the traditional keyword-based information retrieval, extracting the hidden information related to the commodities in photos is a much more complicated and challenging task, involving techniques such as pattern recognition, knowledge base construction, semantic comprehension, and statistic deduction. In this paper, we propose a framework to address this issue by leveraging on various techniques, and evaluate the effectiveness and efficiency of this framework with experiments on a prototype. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1145/2484028.2484120 | SIGIR |
Keywords | Field | DocType |
challenging task,pattern recognition,traditional keyword-based information retrieval,instant reference,data cloud,internet-linked mobile device,knowledge base construction,relevant information,commodity information,hidden information,commodity query,information retrieval,commodity | Data mining,World Wide Web,Statistic,Information retrieval,Commodity,Computer science,Hand held devices,Linked data,Mobile device,Knowledge base,Comprehension | Conference |
Citations | PageRank | References |
0 | 0.34 | 3 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hao Huang | 1 | 89 | 7.77 |
| Yunjun Gao | 2 | 862 | 89.71 |
| Kevin Chiew | 3 | 116 | 11.06 |
| Qinming He | 4 | 371 | 41.53 |
| Lu Chen | 5 | 119 | 29.32 |