We propose a new information sharing system, named "BisNet", which automatically gathers information about the bookmarks stored in users' web browsers and helps the users exchange URIs of possibly interesting web pages with others who have similar interest with them. Being different from other typical agent services that gather and provide information according to pre-registered user profiles, BisNet is expected to share more relevant information because of its use of web browser bookmarks that are actively selected and ordered by many humans. To enhance the relevance of information being shared, we developed a novel algorithm for directory evaluation. This algorithm only looks at the local referential structure between bookmark directories and URIs and calculates for each directory the "order index" that represents how well its content URIs are put in order with a focus on specific areas of interest. Then each directory receives new URIs from other related directories with large order indexes. The repetition of such URI exchanges makes the whole directory-URI networks dynamically form directory groups according to the commonness of the URIs they refer to. Our method is unique in that it pays no attention to the actual contents of web pages, and thus is much simpler and faster than other methods based on the result of content analysis. We carried out a field trial that involved 45 people who used a prototype version of BisNet clients. The result indicated that the relevance of shared URIs positively correlated with the "order index" of surrounding related directories, demonstrating the effectiveness of the method we proposed.
|ジャーナル||Transactions of the Japanese Society for Artificial Intelligence|
|出版ステータス||Published - 2005 5 23|
ASJC Scopus subject areas
- Artificial Intelligence