Aikuchi: Marking-based Social Navigation System⋆

Ryuuki Sakamoto‡

Sadanori Ito♯‡skmt@atr.jp

sito@cc.tuat.ac.jp

Hideaki Takeda ⋆♢‡Kiyoshi Kogure‡takeda@nii.ac.jp

kogure@atr.jp

The Graduate University for Advanced Studies, Shonan, Hayama, Kanagawa 240-0193 Japan♢‡

Yuki Matsuoka⋆♢‡m-yuki@grad.nii.ac.jp

National Institute of Informatics, 2-1-2 Hitotsubashi, Chiyoda-ku, Tokyo 101-8430 Japan

Advanced Telecommunications Research Institute International, 2-2-2 Hikaridai, Seikacho, Soraku-gun, Kyoto 619-0288 Japan♯Tokyo University of Agriculture and Technology, 3-8-1 Harumi-cho, Fuchu-shi, Tokyo 183-8538 JapanAbstract
We propose a social navigation system called Aikuchi thatenables users to link from a marked string in a web page toanother web page and share it with others. Aikuchi highlights a marked string in the web page. It functions as a linkanchor that includes two kinds of links: footprint links thatusers have linked from marked strings in web pages to otherweb pages and recommendation links suggested by the system based on four algorithms. Users can jump from the linkanchor on the web page to other web pages. We presented thesystem at a conference. Based on analysis of user logs, users
preferred footprint links to recommendation links. We foundthat sharing links that users attached from marked strings in
web pages to other web pages is useful for social navigation.

Keywords
Social navigation, web browsing, marking

1. Introduction
Search processes can be very frustrating, especially if wespend a lot of time seeking information. Although people
constantly use the web to ﬁnd information, their experiences
are rarely captured and used to guide or inform others. In
a social navigation system, such as amazon.com [1], whenusers access a page for an item, they can get related items
based on the history of previous purchases. The term “socialnavigation” was originally introduced by [3] to describe howuser navigation through an information space is guided andstructured by the activities of others within that space. A social navigation system enables users to exploit the activitiesof others, to become aware of others’ interests and knowledge, to get recommendations of relevant information basedon the opinions of others, and use web navigation as a kind ofcommunication channel [2]. It also promotes knowledge andinformation exchange among users on the web.
We proposed a social navigation system called Aikuchi thatenables users to move from one web page to other web pages
by links established by other users from marked strings in
web pages to other web pages. In the following sections, weintroduce our system, describe its architecture, and analyze

ICWSM’2007 Boulder, Colorado, USAuser logs.

2. Outline of the system
We use markings as user interactions in our social navigation
system called Aikuchi. When people read books or documents, they often underline words or sentences on pages ofinterest. We call them marked strings; marked strings ona page diﬀer by users, since they have diﬀerent viewpoints.
If they could share the marked strings on pages, they might
discover pages that they would not have found by themselves.
Our system enables users to link from a marked string on a
web page to another web page and share it with others. The
system shares links called footprint links for social navigation.
Users can move from page to page using the traces of others
based on footprint links.
When users link from a marked string on a web page to another web page, the system highlights the marked string thatserves as a link anchor. If the user places her cursor over thelink anchor on the web page, a window pops up and displays
footprint and recommendation links (Fig. 1). To investigate
whether users prefer links they added or links the system recommended, we prepared recommendation links. The system
displays footprint and recommendation links on a window
based on algorithms A-D. We explain these algorithms as follows.
A) Similarity-based recommendation:
we employed calculated page similarity based on TFIDF[5]. If a user places her cursor over link anchors on a
web page, the system recommends high-similarity pages
regardless of the marked string and link anchors.
B) Collaborative ﬁltering:
The system recommends pages with collaborate ﬁltering[4] using the number of link anchors on a web page asuser evaluations of that page. The system recommends
web pages with a high predictive value using the numberof link anchors.
C) Marking-based matching:
If a word in the marked string matches one in the linkanchor strings on other pages, the system recommends
the matched page.

D) Query-based matching:
If a word in the marked string matches one in otherweb page strings, the system recommends the matchedpage.
The system displays at most ﬁve footprint links and two recommendation links for each algorithm. Footprint links areordered from newly added one to the order, and recommendation links are mixed and ordered randomly. The systemdoes not inform users about these algorithms. Users can jumpfrom the link anchor on the web page to another web page.

■Supporting Taxonomic

Link anchor

Knowledge from PersonalMarked Expressions onthe Web Pages
A Novel E-mail Agent:
An Application ofBayesian Networks
Learning from User’sBehaviors
Knowledge
representation using
semantic tags based onframe analysis
Architecture of
feedback SOM forlearning time-series data
Display Knowledge
Filtering Harmful
Information on Knowledge
Searching Website
Discovery ofRelationships among
Concept Hierarchies using
Formal Concept Analysis
Development of aClassifier by Using QAR
Analysis and Application
to Mutagenesis Datasets

60■■■
■

The footprint link
The recommendation links

Fig. 1: Window after cursor placed over link anchor (Wordballoons show English translations of footprint and recommendation links.)

3. Analysis
We presented Aikuchi at the Japanese Society for Artiﬁcial
Intelligence 2006, which was held from June 7th to 9th, 2006.
Target web pages comprised 276 pages that included paper
titles, authors, and abstracts. After completing the experiment, we obtained 324 link anchors on web pages. The results
revealed 88 users who placed their cursors over link anchors
once or more, and 33 users who jumped from link anchors toother pages.
We investigated which links users preferred when placing
their cursor over link anchors. Before and during the conference, footprint links were most often selected (Fig. 2). We
found that people preferred footprint links to recommendation links for moving from web page to another web page.
This means that users trust links added by other users morethan links recommended by the system. Therefore, sharing
links that users linked from marked strings in web pages toother web pages was useful for social navigation.

D u rin g th e c o n fe re n c e(6 / 7 - 6 / 9 )100ABCDF o o tprin tlin ks

R e c o m m e n datio n lin ks an d F o o tprin t lin ks■■

B e fo re th e c o n fe re n c e(5 / 2 2 - 6 / 6 )sk 5 0inl
de 4 0tceles 3 0fo
re 2 0bmuN

Fig. 2: Number of selected links when users placed cursors
over a link anchor

4. Conclusion
We developed a system called Aikuchi in which users can linkfrom a marked string in a web page to another web page andshare it with others. The system highlights a marked stringthat functions as a link anchor, and users can get information to see the web page that includes them. We found thatlink anchors are useful for users to move pages because footprint links are frequently selected by users. Footprint links
left eﬀective traces of users moving from web page to otherpages. Consequently, we believe that sharing them with users
is appropriate for social navigation.

Acknowledgment
This research was supported by the National Institute of Information and Communications Technology.

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