Shizuku2.0: Community Oriented Reading Support System
Mao TSUNEKAWA（Student Number200921740）

Supervisor: Norihiko UDA
Asst. Supervisor: Atsushi MATSUMURA

1.

Introduction

efficiently and mutually. The system has following

The relationship between the library and thethree functions.

community has a great influence on the design ofthelibrary

automation

system.

The

recentmovement toward Learning CommonsandReading–afunction

that

semi

automatically records users’ reading history

Library 2.0 has increased the importance of thecommunity in the library. It is necessary that library

Streamand distributes it to other users
User Recommendation–a function thatengineers consider the community of library users

recommends similar readers to users, in orderwhen they develop a new library automation system.

to facilitate formation of the reader community

In this study, we propose cooperative reading,

Watch List - a function that allows one to gather

which is a reading support technique that allows

other readers' reading

streams on Shizuku2.0

library users to help each other. We also developedby registering recommended users

a system named Shizuku2.0: Community OrientedReading Support System.

To achieve cooperative

reading, it is necessary for a user to discover otherswith similar interests. Therefore, we develop andevaluate

arecommendation

functionthatrecommends similar users using Nippon DecimalClassification (NDC) Tree Profiling.

2.

Research Question

3.2

NDC Tree Profiling
We also designed and developed NDC Tree

Profiling, which enables the creation of library user
profiles,for

thepurposes

oftheuser

recommendation mechanism.
Concretely, NDC Tree Profiling extracts NDC
codes included in the reading stream of each userand constructs a hierarchical structure based on theThe following two research questions weredata, constituting a user profile. For instance, whenaddressed: Is the user recommendation using NDC

a user has three NDC codes: 410.02, 421, 913.02

Tree Profiling effective in finding similar users?

and 914, NDC tree is shown as Figure 2.

Which parameter of NDC Tree Profiling method is

Shizuku2.0

the most effective expression of users’ interests?

calculating the similarity of these hierarchicalrecommends

similarusersbystructures.

3.
3.1

MethodShizuku2.0
We developed the Shizuku2.0 system (Figure1) to support the creation of a library user
community in which users help each otherToverifytheeffectof

theuserrecommendation mechanism, we performed anexperiment with 37 student users to calculate recall
and precision.

Figure１: Shizuku2.0

we found the second level of the NDC code could
be the most effective expression of users’ interests.
In the discussion of the optimization of parameters,parameters, we propose a new way of implementing
the NDC Tree, based on the second division ofNDC, which is expected to improve creation of user
profiles.

Figure 2: An Example of NDC Tree

4.

Findings
We found that the recommendation using NDC

Tree Profiling is more

effective than a random

recommendation. However, we also recognized that
there is room for improvement relative to a past
information recommendation technique. Moreover,