Sharing Bookmarks among Same Interest Persons
Mikihiko Mori and Seiji Yamada
CISS, IGSSE, Tokyo Institute of Technology
4259 Nagatsuta, Midori, Yokohama 226-8502, Japan
mori@ymd.dis.titech.ac.jp
Abstract: We propose Bookmark-agent system that enables users to share information in eachuser’s bookmarks with others. This system is more effective in finding specific information that isa common interest of all the members in a group than existing search engines, such as Yahoo andAltaVista. When a user wants to find certain URLs, the Bookmark-agent will find them on behalfof the user. In addition to that, the Bookmark agent requests other Bookmark agents to search
their own bookmarks. As a result, the user can obtain information that best matches his/her needsfrom the Bookmark-agent. Since users filter the selected information beforehand, it is possible
for the Bookmark-agent to satisfy a users’ requests more precisely than existing search engines.
After conducting experiments on six users, we have found that the Bookmark-agent system is apromising approach to share URLs in a small community.

1Introduction

In this paper, we propose Bookmark-agent system. The agent, which represents each user, uses user’s bookmarks toget he/her interest. The bookmark is URL as filtered information from all Web pages in the WWW. In a small usergroup having the same interest, the agents share bookmarks of each user as their interest through communication
in between them. The group has been gathered beforehand. A Bookmark-agent assists a user to search for thesites that resemble a Web page visited. The agent refers user’s bookmarks and others through communication
in between them if users allowed the bookmarks to be open to public. There are no need to give any keywords
to the agent, because the agent can extract some keywords from the Web page a user has just visited. Note thata Bookmark-agent gives its user a smaller number of more significant URLs than the result given by ordinary
search engines, because the agents utilize bookmarks, which are filtered information of the WWW by users, andare only used in a small group sharing the same interest. Keywords that represent users’ interest make the numberof presenting URLs less than the result given by ordinary search engines.
Siteseer (Rucker & Polanco 1997) utilizes each user’s bookmarks as an implicit declaration of the interestin the underlying content. The difference from Bookmark-agent is that Siteseer measures the degree of overlap
in URLs between each user’s folder and other people’s folders. WebMate (Chen & Sycara 1998) learns user’s
interest incrementally and automatically sends mails that match user’s interests. Moreover, it adds keywords froma user profile to search for URLs of interesting sites. WebMate monitors user’s browser to get user’s profile, whileBookmark-agent makes profile from bookmarks. Beehive (Huberman & Kaminsky 1996) designs a distributed
system for social sharing and filtering of information on email system. Information of interest by a user is sent tomembers of listed groups through Beehive system. The member who does not communicate is deleted from themember list, if the frequency of his writing messages is lower than a threshold. Content of message is no concernof this system.

2

Bookmark-agent

An Bookmark-agent is invoked for each user and assists searching the WWW. Functions of Bookmark-agent arethe followings:• Extracting keywords from a new URL.
• Indicating URLs of similar pages [1] .
Searching for similar pages requires the following processes:– Searching the user’s keyword-database.
– communicating and requesting to other agents in order to search other users’ keyword-databases.
• Accepting a search request.
We define a set of keywords for a Web page. Each page consists of words surrounded by tags, which weighs thewords. We prepare a set of Keyword-weight pairs. The set has no duplication of the same keyword, and weights
for the same word are added together. The top 5 keyword-weight pairs are selected to be used as the keyword setof the Web page. The following tags and weight assigned to each tags (in parenthesis) are used to calculate theweight for each keywords: CONTENT attribute in <META> tag (10), <TITLE> tag (10), <H1> tag (6), <H2> tag(5), <H3> tag (4), <EM> tag (1), <U> tag (1).
[1] defined as a page whose similarity is over a threshold.

The similarity between two Web pages is defined as the number of elements in the product set in between thekeyword sets of these two Web pages. The similarity varies from 1 to 5.

3Experiments

We calculate precisions when one to three keywords in the databases are given to an agent. We chose the keywords
that are surely contained in the keyword-databases of all users.
For comparison with Bookmark-agent, the search engine Goo [2] is used, since Goo answers a lot of URLs anduses a Web robot for retrieval. Our assumption is that Goo’s capability of answering a lot of URLs must havecovered all area of ordinary interest. It is not easy for users to find necessary URLs out of many URLs searchengines recommend. To solve this problem, some search engines present URLs in the order sorted by priority toa user. To evaluate precision from the proportion of the retrieved documents that is actually relevant, if the searchengine answered 20 URLs or more, we decided to use the URLs from the top to the 20th.
For evaluating the performance of Bookmark-agent, we prepared six users as testees who had 782 bookmarks
as a whole. These users used Bookmark-agent and had already their own keyword-database.
In these experiments, a user inputs keywords to Bookmark-agent or the search engine, Goo. The relevance ofeach URL presented was judged from his point of view.

4Discussion on The Experimental Results

A keyword used in a group sharing concept is presented higher precision than one is not used in the group. For
instance, ‘agent’ usually means that the person who takes user’s place or give service for users who cannot do.
However, it is clear that the ‘agent’ in the group of testees means agent system in AI. The precision of keyword‘agent’ in the search engine Goo was half or less than average. However, the precision of ‘agent’ in Bookmarkagent did not change, regardless of the number of keywords. Therefore, it is reasonable to suppose that users havecommon sense regarding the keyword, and it is considered as that the users share preference.
The rank sum test between precision of each keyword from Bookmark-agent system and that from Goo wasconducted for each case of Table 1 to 3. As a result, we found a difference between the result of Bookmark-agentand that of Goo on significance level of 5% in each test. Thus, the method of the search engine Goo did not haveability of that of Bookmark-agent.
First, we pay attention to the preference that is shared in the group. Although the user in the experiments does
not share preference in general sciences, they have common sense in AI, robot and agent system. Which is thereason that the precision of ‘ai’, ‘robot’ and ‘agent’ are higher than that of ‘science’. The keyword in which theyshare preference presents more URLs than the one in which they do not share.
Next, we pay attention to the preference that is not shared. The keywords ‘robot’ and ‘linux’, the query ofwhich returned 10 and 22 URLs respectively, are the words which concepts are shared among the users. On theother hand, they do not share the concept of the word ‘perl’, because only the user C have preference on ‘perl.’
As a result, the keyword ‘perl’ returned only 3 URLs. Normally, a user must have been indicated more URLs to‘unix’ than to ‘linux’. The keyword ‘linux’, however, returned more URLs than ‘unix’, because the number of theusers using Linux is more than that of using Unix.

5Conclusion

We proposed Bookmark-agent, which uses bookmarks to support users’ searching for information, and conducted
experiments on six users. Sharing URLs of each user’s bookmarks, which helps indicating necessary Web pages,
Bookmark-agent provides a few but significant URL as a search result. Bookmark-agent is clearly different fromgeneral search engines on the point that search engines often give a large number of URLs that often contains
irrelevant information to users. By using bookmarks, Bookmark-agent is able to provide a small number of URLs
to a user who shares preference with other users.

References
Chen, L. and Sycara, K. (1998). WebMate: A Personal Agent for Browsing and Searching, Proc. 2nd Int. Conf. AutonomousAgent, 132–139.
Huberman, B.A. and Kaminsky, M. (1996). Beehive: A System for Cooperative Filtering and Sharing of Information,
http://www.parc.xerox.com/spl/groups/ dynamics/www/beehive.html.
Rucker, J. and Polanco, M.J. (1997). Siteseer: Personalized Navigation for the Web, Commun. ACM, 40 (3), 73–75.
[2] http://www.goo.ne.jp/