Search (1 results, page 1 of 1)

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Abstract

As the Web has become a major channel of information dissemination, many newspapers expand their services by providing electronic versions of news information on the Web. However, most investors find it difficult to search for the financial information of interest from the huge Web information space-information overloading problem. In this article, we present a personal agent that utilizes user profiles and user relevance feedback to search for the Chinese Web financial news articles on behalf of users. A Chinese indexing component is developed to index the continuously fetched Chinese financial news articles. User profiles capture the basic knowledge of user preferences based on the sources of news articles, the regions of the news reported, categories of industries related, the listed companies, and user-specified keywords. User feedback captures the semantics of the user rated news articles. The search engine ranks the top 20 news articles that users are most interested in and report to the user daily or on demand. Experiments are conducted to measure the performance of the agents based on the inputs from user profiles and user feedback. It shows that simply using the user profiles does not increase the precision of the retrieval. However, user relevance feedback helps to increase the performance of the retrieval as the user interact with the system until it reaches the optimal performance. Combining both user profiles and user relevance feedback produces the best performance

Source

Journal of the American Society for Information Science and technology. 53(2002) no.2, S.186-196