=Paper=
{{Paper
|id=Vol-209/paper-17
|storemode=property
|title=Social tagging using marked strings in web pages
|pdfUrl=https://ceur-ws.org/Vol-209/saaw06-poster04-matsuoka.pdf
|volume=Vol-209
|dblpUrl=https://dblp.org/rec/conf/semweb/MatsuokaSI0K06
}}
==Social tagging using marked strings in web pages==
https://ceur-ws.org/Vol-209/saaw06-poster04-matsuoka.pdf
Social tagging using marked strings in Web pages
Yuki Matsuokaⅰⅱⅲ, Ryuuki Sakamotoⅲ, Sadanori Itoⅳⅲ, Hideaki Takedaⅰⅱⅲ, Kiyoshi Kogureⅲ
ⅰThe Graduate University for Advanced Studies,
Shonan Village, Hayama, Kanagawa 240-0193 Japan
ⅱNational Institute of Informatics,
2-1-2 Hitotsubashi, Chiyoda-ku, Tokyo 101-8430 Japan
ⅲAdvanced Telecommunications Research Institute International,
2-2-2 Hikaridai, Seikacho, Soraku-gun, Kyoto 619-0288 Japan
ⅳTokyo University of Agriculture and Technology,
3-8-1 Harumi-cho, Fuchu-shi, Tokyo 183-8538 Japan
E-mail : m- yuki@grad.nii.ac.jp
ABSTRACT words that differ from what users intended. One possible
In this paper, we propose a system called “Aikuchi,” which solution to this is to employ user-generated tags from Social
enables users to mark a string on a Web page and share it with Bookmark Services (SBS), enabling users to easily find
other users. If users mark a string on their Web browser using appropriate pages. However, as users freely add tags, numerous
their mouse cursor, Aikuchi recommends links to other Web other types of tags also accumulate on Web pages.
pages based on recommendation algorithms. When a user clicks Because users view each page subjectively, they add tags to
a recommended link, Aikuchi highlights a marked string. We express the contents, their feelings about the page, the purpose of
offered the system as part of a conference support system. the page, and so on. When users find Web pages using such tags,
According to the analysis of user logs, users preferred Web page though, such diversity becomes an obstacle to finding pages that
recommendations based on strings marked by other users to those are actually appropriate. In this research, we employ strings
based on page similarity using TFIDF and collaborative filtering. marked by users as tags to exclusively express a given page’s
As a result, we think that marked strings can act as user- contents. The system we have implemented, called Aikuchi,
generated metadata. enables users to mark strings in Web pages and share them with
other others. If a user marks a string in the Web browser using
Categories and Subject Descriptors his or her mouse cursor, Aikuchi recommends links to other Web
H.3.3 [Information Search and Retrieval]: Search process, pages by applying it recommendation algorithms, and when a
Selection process; H.3.5 [Online Information Services]: Data user clicks a recommended link, the system highlights the
sharing marked string, which is called a footprint. The recommendation
algorithms are based on page similarity using TFIDF,
collaborative filtering, and word matching using words in the
General Terms footprint or another Web page. We offered the system as a part of
Experimentation, Human Factors a conference support system and analyzed whether users prefer
the recommendation algorithm based on marked strings to search
Keywords Web pages.
Social tagging, marking, user-generated metadata
2. RELATED RESEARCH
1. INTRODUCTION Semantic annotation systems have previously been proposed
It goes without saying that there is an enormous number of that highlight strings in Web browsers. One example is CHOSE
pages on the Internet; therefore, we often need to use search [1], which stores metadata as hyperlinks in a Distributed Links
engines to find ones that interest us. However, search results Service [2] and uses it to highlight ontology terms in the text
frequently include “noise” pages. While search engines do return strings on the Web browser. Magpie [3] highlights strings related
pages matching users’ query words, some pages may include to the ontology of the user’s choice on the browser. Both systems
highlight strings to make the connection between a string and an
ontology. Our system links from a string to offered pages and
Permission to make digital or hard copies of all or part of this work for
differs from them by the lack of connection between the link and
personal or classroom use is granted without fee provided that copies are
not made or distributed for profit or commercial advantage and that copies a meaning; it is instead related to the user’s interest. In this
bear this notice and the full citation on the first page. To copy otherwise, or paper, we reuse a selected string as metadata to search Web
republish, to post on servers or to redistribute to lists, requires prior specific pages.
permission and/or a fee.
SAAW’06, November 6, 2006, Athens, GA, USA.
Copyright 2006 ACM 1-58113-000-0/00/0004…$5.00.
3. A SOCIAL TAGGING SYSTEM
3.1 Overview
We propose a system called Aikuchi that enables users to mark
strings in Web pages and jump to other pages from the marked
string. The system lets users search pages using marked strings
from other users.
Details of the system are as follows: Users can mark a string in
their Web browser using their mouse cursor when they find
something of interest on a page. (Fig. 1). Then the
recommendation window pops out to display recommendations
from other pages called recommendation links (Fig. 2). If the
user selects a recommendation link, the system shows the
specified page, and once a recommendation link is selected, the Figure 3. The footprint
system highlights the used string. We call this highlighted string The right figure is translation of the left it.
a footprint (Fig. 3). It is shared; i.e., users can see the footprints
added by all other users. If the user places her or his mouse
cursor over the footprint, the recommendation window pops out
again, with Fig. 4 Showing examples of recommendation links
and footprint links. The latter includes Web pages to which some
users have jumped from the marked string.
Figure 4. Recommendation window after the cursor is
placed over the footprint
The right figure shows translation of the footprint link and the
recommendation links
Figure 1. Marking on the Web browser
The left figure indicates title, authors and abstract for a paper. 3.2 Implementation
The right figure is translation of the left it. Figure 5 shows the system’s structure. The system runs as a
script on the user client and on the Web server, and is
implemented with JavaScript and PHP.
When a user accesses a Web page, the JavaScript engine asks
the Web server if a footprint exists, and shows it if that is the
case. When a user marks a string in the Web browser, the
JavaScript engine obtains the marked string and sends it to the
Web server, which then takes the marked string and calculates
the recommendation links according to certain algorithms.
Following calculation, the Web server sends recommendation
links and the JavaScript engine shows the recommendation
window to the user. If the user selects a recommended link, the
JavaScript engine sends it and the Web server saves the
following information related to the selected link as a text file in
the metadata storage.
Date
User ID
Figure 2. Popped-out recommendation window
The right figure shows translation of the recommendation links Marked string
Position of the marked string on the Web page
URL of the selected recommended link find useful pages based on the string or footprints. To make
recommendations, we use the system in a closed environment
Selected recommendation algorithm
where the available Web pages are fixed1.
The algorithms are as follows:
A) Page similarity using TFIDF
B) Collaborative filtering using the number of footprints on a
Web page
C) Word matching between a marked string and footprint’s
strings
D) Word matching between a marked string and the Web
page’s strings.
In algorithm A, the system employs calculated page similarity
based on TFIDF [4]. We calculate the TFIDF value of words on
Web pages and cosine similarity using them. If a user marks a
certain string on a Web page, the system recommends high-
similarity pages to it regardless of the marked string and
footprints.
In algorithm B, the system recommends pages with collaborative
filtering [5], using the number of footprints on a Web page as the
users’ evaluation of that page. We adopt not incoming links but
outgoing links, because selected recommendation links are not
valuable for users. Therefore, the system recommends Web pages
with a high predictive value using the number of footprints.
In algorithm C, if a word in the marked string matches one in
the footprint’s string on other pages, the system recommends the
matched page.
In algorithm D, if a word in the marked string matches one in
another page’s string, the system recommends the matched page.
Table 1 shows a comparison of recommendation algorithms with
respect to whether they use a marked string and footprints.
Table 1. Comparison of recommendation algorithms
Recommendation A marked string Footprints
algorithm
A × ×
B × ○
C ○ ○
D ○ ×
Algorithm A makes recommendations based on page similarity
between Web pages without using a marked string or footprints.
Figure 5. System structure
This is assumed to be ordinary recommendation. Algorithm B,
When a user places his or her mouse cursor over a footprint, the meanwhile, uses the number of footprints on a Web page to look
JavaScript engine takes the string of the footprint and sends it to for neighborhood users, and algorithms C and D use a marked
the Web server. The Web server gets the footprint’s string, string as a query to search Web pages with matching words. The
calculates recommendation links, and obtains footprint links difference is the search target: a footprint’s strings or a Web
from the metadata storage. The Web server then sends both page’s strings.
footprint links and recommendation links. Next, the JavaScript We investigated which algorithms are preferred by users. The
engine again displays the recommendation window to the user system recommends up to two Web pages for each algorithm and
and if the user selects a footprint link or a recommended link, the shows them in random order. Algorithm priority was applied so
JavaScript engine sends it and the Web server saves information that the same link was calculated in order of C, D, B, and A. The
about it. system did not inform users about these algorithms. When users
clicked a recommended page link, we determined that users
3.3 Recommendation algorithms preferred its recommendation algorithm.
When a user marks a string or places the mouse cursor over a
footprint on a Web page, the system shows recommendation 1
Aikuchi is not limited to closed environments, but we restricted
links using four types of algorithms that explore Web pages to
the system to test recommendations in the following test cases.
4. ANALYSIS previously but also jumped pages. We investigated which
We offered Aikuchi as a part of a support system at the algorithms are preferred by users in such cases (Fig. 7). The
Japanese Society for Artificial Intelligence 2006, which was held algorithm Z denotes the selection of jumped pages by users, and
from June 7th to 9th, 2006. The support system was operated as a the figure shows that Z was selected in an overwhelming number
Web system, and every conference participant could access it of cases. Therefore, we found that users prefer to select links by
using a user ID and password. The target Web pages comprised using footprints.
276 pages that included authors and abstracts for papers. We
prepared recommendation pages using algorithm A. When users 80
marked a string, the system took a few seconds to display 70
recommendation links. After the completing the experiment, we B e fo r e a n d D u r in g t h e
60
c o n fe re n c e
obtained 324 footprints and 172 links from them. Analysis of the 50
results revealed that there were 45 users who marked strings one 40
or more times, 28 users who jumped from marked strings to 30
other pages, 88 users who placed their mouse cursors over 20
footprints one or several times, and 33 users who jumped from 10
footprints to other pages. In this section, we describe which 0
A B C D Z
algorithms were preferred by users.
Figure 7. Number of selected recommendation algorithms,
4.1 Preference of the algorithms when users placed their mouse cursor over a footprint.
Figure 6 shows a comparison of the number of selected
recommendation algorithms before and during the conference. 5. CONCLUSION
Before the conference, algorithm A was most commonly selected We proposed to use strings marked by users on Web pages as
and the number of selections of B and C were few since the tags, and developed a system called Aikuchi with which users
footprints were few at the beginning the conference. The number can mark strings and share them as footprints. Aikuchi
of selections for D was fewer than that of A before the recommends links based on a variety of algorithms when a user
conference. This means users selected a recommended link based marks a string on a Web page.
on similarity without relation to marked strings or footprints.
During the conference, the number of selections of C and D were Based on the analysis of user logs, we found that users
more than before it and the number of selections of B was fewer preferred recommendations based on words in marked strings
than before it, since if Aikuchi recommended the same page links rather than page similarity. We consider that footprints are useful
over time, then users would be tired of selecting the links for users to select links because links based on footprints are
recommended by B. The number of selections for algorithm A frequently selected by users. Consequently, we believe that
was also lower during the conference. This means users selected strings marked by users on Web pages can be used as user-
the recommendation algorithms based on the word in the marked generated metadata for searching pages.
string. Therefore, we found that users prefer recommendations
based on words rather than similarity over time. As the number 6. ACKNOWLEDGMENTS
of selections for algorithm C increased, we also found that This research was supported by the National Institute of
footprints are effective for recommending links. Information and Communications Technology.
30
7. REFERENCES
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