Extracting and Summarizing Hot Item Features Across Different Auction Web Sites

Online auction Web sites are fast changing and highly dynamic. It is difficult to digest the poorly organized and vast amount of information contained in the auction sites. We develop a unified framework aiming at automatically extracting the product features and summarizing the hot item features across different auction Web sites. One challenge of this problem is to extract useful information from the product descriptions provided by the sellers, which vary largely in the layout format. We formulate the problem as a single graph labeling problem using conditional random fields which can model the relationship among the neighbouring tokens in a Web page, the tokens from different pages, as well as various information such as the hot item features across different auction sites. We have conducted extensive experiments from several real-world auction Web sites to demonstrate the effectiveness of our framework.