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Learning to extract and summarize hot item features from multiple auction web sites

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Abstract

It is difficult to digest the poorly organized and vast amount of information contained in auction Web sites which are fast changing and highly dynamic. We develop a unified framework which can automatically extract product features and summarize hot item features from multiple auction sites. To deal with the irregularity in the layout format of Web pages and harness the uncertainty involved, we formulate the tasks of product feature extraction and hot item feature summarization as a single graph labeling problem using conditional random fields. One characteristic of this graphical model is that it can model the inter-dependence between neighbouring tokens in a Web page, tokens in different Web pages, as well as various information such as hot item features across different auction sites. We have conducted extensive experiments on several real-world auction Web sites to demonstrate the effectiveness of our framework.

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Authors and Affiliations

  1. Department of Computer Science, City University of Hong Kong, 83 Tat Chee Avenue, Kowloon, Hong Kong

    Tak-Lam Wong

  2. Department of Systems Engineering and Engineering Management, The Chinese University of Hong Kong, Shatin, Hong Kong

    Wai Lam

Authors
  1. Tak-Lam Wong
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  2. Wai Lam
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Correspondence to Tak-Lam Wong.

Additional information

The work described in this paper is substantially supported by grants from the Research Grant Council of the Hong Kong Special Administrative Region, China (Project Nos: CUHK 4179/03E and CUHK4193/04E) and the Direct Grant of the Faculty of Engineering, CUHK (Project Codes: 2050363 and 2050391). This work is also affiliated with the Microsoft-CUHK Joint Laboratory for Human-centric Computing and Interface Technologies.

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Wong, TL., Lam, W. Learning to extract and summarize hot item features from multiple auction web sites. Knowl Inf Syst 14, 143–160 (2008). https://doi.org/10.1007/s10115-007-0078-2

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