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Abstract

Surveying is essential for conducting research. While searching engines offer

information retrieving functions, How to find related knowledge efficiently and

correctly, are our key concerns here. Proper filtering and guiding mechanisms

assist readers greatly. This work design and implements a recommender system for

digital literature retrieval. Relational indexing technique is used to analysis

relationships between document attributes, constructing relation indexes. Basing on

the information, algorithms for providing recommendations of relevant information

including authors-alike, related-search terms, and recommend-read are designed.

The system framework is designed and implemented into a DSpace-based

departmental ETD system, and tested with existing document pools.

Theses/dissertations from 2001~2005 of the department of the information

engineering and computer science at FCU(Feng Chia University) were analyzed and

In addition to providing recommendation to scholarly literatures, trend analysis

information trend information gathered can also be used for institutional

self-assessment.

Abstract ...................................................................................................................... i Chapter 1 Introduction ...............................................................................................1 Chapter 2 Related work..............................................................................................3

2.1 Recommender Systems ................................................................................3 2.2 DSpace.........................................................................................................3

Chapter 3 Recommendation Schemes ........................................................................5 3.1 Basic Flow ...................................................................................................5 3.2 Recommending Literatures and authors........................................................6 3.3 Recommending of Terms..............................................................................7

Chapter 4 System Architecture/Implementation .........................................................8 4.1 Architecture..................................................................................................8 4.2 System Implementation ................................................................................9

Chapter 5 System Prototype ..................................................................................... 11 Chapter 6 Experimental Results ............................................................................... 14 Chapter 7 Conclusion............................................................................................... 15 Reference................................................................................................................. 16 �� ........................................................................................................................ 19

Appendix ................................................................................................................. 21

Fig 1 Recommendation Flowchart .............................................................................5 Fig 2 Recommendation of Literature/Author..............................................................6 Fig 3 Recommendation of Keywords.....................................................................7 Fig 4 S ystem A rch itectu re.......................................................................................9 Fig 5 Implementation component/platforms ...............................................................9 Fig 6 Main screen .................................................................................................... 11 Fig 7 Search Result .................................................................................................. 11 Fig 8 Recommendations........................................................................................... 12 Fig 9 Recommended Authors ................................................................................... 12 Fig 10 Recommend keyword ................................................................................... 12 Fig 11 Annual Research Trends................................................................................ 13 Fig 12 Faculty Profile Examinations ........................................................................ 13 Fig 13 Level of Satisfaction ..................................................................................... 14

1

Chapter 1 Introduction

By the advance in network and digital archival technology, large, full-scale

digital libraries are now common worldwide, where literatures and sources are stored

and preserved digitally, and can be accessed through the Internet [20]. Digital capacity

made it possible to provide services such as information seeking and filtering [8],

organizing [2], and information retrieval/search [5].

While surveying, digital literatures in a hosting portal are usually indexed by

content-based information such as authors, publish date, and keywords. Although

comprehensive, searching via these methods usually require not only that the users

have a good understanding of whatever research domain/topic they are looking for,

but also how these topics are indexed in the system. This puts novice students or

cross-disciplinary researchers at a disadvantage. Those who are unfamiliar with the

domain, having little choice of words, either make their queries over-specific,

resulting in scarce items, or a search conducted with fundamental terms such as

network, Internet, which would yield too data to be effective [10]. There is also the

possibility that given index terms are insufficient to represent the features of the

document [1], (such as synonyms that are seldom used). Due to bad indexing, these

documents may never be found via the retrieval mechanism, though the user might be

interested in its content. The focus of this research is to deal with information

overload. Proposed solutions to this emphasize the need for specialization in

information retrieval services, to help people effectively locate information that meets

their individual needs.

This work design and implements a recommender system for digital literature

retrieval. Relational indexing technique [4] is used to analysis relationships between

document attributes, constructing relation indexes. Basing on the information,

2

algorithms for providing recommendations of relevant information including related

authors, related keywords, and related-read are then functional. The proposed system

framework is designed and implemented onto an existing DSpace-based departmental

ETD (Electronic Theses and Dissertations) system, and tested with existing papers. In

addition to providing effective filtering and guideline services which help researcher

greatly, statistical and trend information gathered from documents can also be used

for trend analysis [7] or institutional self-assessment [12-13].

The rest of this paper is organized as follows: Section 2 discusses related works

concerning recommender systems. Recommendation schemes are described in

Section 3. System architecture/implementation is described in Section 4, while system

prototype is described in Section 5. Analysis results described in Section 6, and finally

Section 7 concludes the paper.

3

Chapter 2 Related work

Related work is described in this chapter. Recommendation system is described

in Section 2.1, DSpace is described in Section 2.2.

2.1 Recommender Systems

Recommender systems [9] are systems which attempt to predict commercial items

such as movies/TV programs [11, 14], music [3, 24, 26], books [21], news, literature

recommenders [1, 6, 16, 18], or web pages that a user may be interested in, or can be

used to provide guidance in IR such as related search [6] or query correction so that

better search results can be yields. There are mainly two ways to conduct

recommendation: content-based methods analyze the content and attributes of the

items such as authors, cast, singers, publish date, full-text body [15], while

collaborative methods [17] make predictions based on user data such as web logs,

usage records, level of popularity, access frequency, users with similar access history,

etc. In the Virtual Classroom for Chronobot developed by S. K. Chang [4], relational

indexing and user profiling techniques are used to perform hybrid query matching,

where as teleconference logs are analyzed to extract users’ research interests, and to

match them with jobs/requests posted on Chronobot.

2.2 DSpace

Co-developed by MIT and HP Labs, DSpace [22] is an open source software

package which provides the tools for management of digital assets, and is commonly

used as the basis for an institutional repository [19]. DSpace provides management

infrastructure, IR functions, as well as submission DSpace is written in Java and JSP,

using the Java Servlet Framework. It employs a relational database, and supports the

use of PostgreSQL and Oracle. It makes its holdings available primarily via a web UI,

but also supports OAI-PMH v2.0 [25], and is capable of exporting METS (Metadata

4

Encoding and Transmission Standard) packages also. Growing number of universities

use DSpace for managing digital documents in libraries. It is currently being

experimentally used in FCU Library, and recommender features are desired in order

to help students/staff in finding books/e-papers of their liking.

5

Chapter 3 Recommendation Schemes

Schemes for recommendation are described in this chapter. In the section 3.1, the

flowchart will be described. The algorithm which be used to recommend related

document and authors and is in Section 3.2. In the section 3.3, the algorithm of

recommending keywords will be described.

3.1 Basic Flow

Shown in Fig. 1 is the basic flow of literature recommendation, which contains

several steps:

Phase1: A query such as keywords, authors, or other information the user interested

in, is acquired via UI, and items are then retrieved via IR routine provided by the

repository system.

Phase2: Search result is transferred to the recommender module for relation

extraction. The relation here is defined as a set of items ordered by relativity (which

can be custom-defined). Immediate relations including area relation and keyword

relation can be extracted directly from search results.

Query

S ea rc h R es ul t

A reaR el a t i o n

D o c um en t a t i o n R el a t i o n

A ut h o r R el a t i o n

K eyw o rd R el a t i o n

Search

R el a t i o n A n al y s i s

Recommender Module

A cq u i re U s er I n p u t

R e t ri e v a l

Recommendations

Recommendation F l ow ch ar t

Fig 1 Recommendation Flowchart

Phase3: The Recommender System analyzes the area relation, and Documentation

Relation and Author Relation are then derived.

Phase4: Recommendations are generated from relations (usually the top several items

6

are considered most-related, thus are recommended), related information are retrieved

from database, and output to UI.

3.2 Recommending Literatures and authors

Q

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Fig 2 Recommendation of Literature/Author

The recommending framework for literatures and authors shown in Fig. 2 is

described as follows:

Phase1: documents that have query Q is searched, generating paper set P from the

database.

Phase2: Each paper pi contains following attributes: {Ei: author(s), Ri: research area,

Kj: keyword set, Di: date}. AP denotes the set which contains all Ai from P which is

then sorted by each Ai‘s relativity with Q, denoted by rev(Q, Ai), thus generating RAQ,

the ordered area relation of Q.

Phase3: Finally, top research areas (in our case, three) in RAQ are used as queries and

send to search engine to extract recommended literatures P’ and authors T’ from

database.

7

3.3 Recommending of Terms

Q

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Fig 3 Recommen d a t i on of K ey w or d s

The recommending framework for terms shown in Fig. 3 is described as follows:

Phase1: documents that have query Q is searched, generating paper set P from the

database.

Phase2: Each paper pi contains following attributes: {E: author(s), R: research area,

K: keyword set, Di: date}. K is extracted from each paper and joined as a keyword

pool KP (Note that keywords redundant with Q are removed). KP is then sorted by

each Ki‘s relativity with Q, denoted by rev(Q, Ki), thus generating RKQ, the ordered

keyword relation of Q.

Phase3: keywords with highest relativity are picked as candidates for recommended

search terms. Their actual names are then retrieved from database, forming K’. Note

that due to the large numbers and scarce repeating of keywords, there may be several

items with the same relativity to Q. Randomness can be introduced to avoid repeated

recommendation.

8

Chapter 4 System Architecture/Implementation

In this chapter, the system architecture will be explained in section 4.1. Section

4.2 describes the implementation of system, including the data that we used and our

developing environment.

4.1 Architecture

The proposed recommender system is designed as a component subsystem of a

standalone digital portal such as DSpace. The system architecture can be divided into

application layer, logic layer, and storage layer, as is shown in Fig. 4. The Application

layer covers Web user interface, which is designed to display searched/recommended

items, page switching, various commands, messages, and input boxes to interact with

user.

The Logic layer is composed of search engine, recommender system, and content

management. While search engine responses for IR, accepting tokens as query keys

and translate them to database commands. Content management for reading and

manipulating content stored, in the literature portal. The Storage layer is composed of

the database, the digitally archived literatures, and other media files, all integrated and

controlled by either DB drivers or file manager.

The recommender system is insinuated a as a standalone subsystem in the logic

layer. It must be able to accept query from the Web UI, or the search results from the

search engine in order to get information needed for execution. When the search

engine acts, the recommender will be triggered and catch the search result, and

perform relation mining described earlier. According to schemes described in the

previous section, it must also be able to communicate with content management

component to access database. After recommendations are done, the results must also

be output to Web UI.

9

The statistical tools in the Application layer can be manipulated to extract

information such as Annual Research Trends, which lists the number of literatures in

every research area in the system’s category, showing the trend of the institution. It

can also generate Personal Research Trends, which extracts an authors’ literatures.

Logic Layer

DB Docs

Application Layer

Storage Layer

Search Engine

DB Driver

Recommender

Web User Interface

File Manager

Statistical Tools

Content Mgr.

Fig 4 S y st em A r ch i t ect u r e

4.2 System Implementation

The original departmental DSpace in FCU architecture system was building on

Linux. For safe development without endangering the original system, its contents are

duplicated to a new Microsoft Windows 2003 server installed with Apache, Jakarta

Tomcat, and the PostgreSQL DBMS specified by DSpace, as shown in Fig. 5. The

recommender component was developed in ASP .NET/C#. Original JSP/Java

components in the DSpace were modified

Microsoft IISJakarta TomcatWeb server Microsoft IISJakarta TomcatWeb server

RecommenderDSpaceTool Options RecommenderDSpaceTool Options

Windows server 2003Operating System Windows server 2003Operating System

ASP .NET / C#JSPProgramming Language ASP .NET / C#JSPProgramming Language

PostgreSQLDBMS PostgreSQLDBMS

Fig 5 Implementation component/platforms

For recommendation component, the relativity function rev(Q, Ki) is defined

according to the number of occurrences in

10

The archive collects theses/dissertations of the Graduate Institution of Information

Engineering and Computer Science of FCU from year 2001~2005. Totally 245 papers,

covering 18 research areas (according to research areas issued by National Science

Committee [27]) which are : Computer Structure and Operating System,

Programming Language and Software Engineering, Computer Network, Computing

Theory and Algorithm, Parallel and Distributed Processing, Information Security,

System Modeling and Imitation, GNU, Image and Graph Identification, Natural

Language Processing and Phonation Processing, Artificial Intelligence, Computer

Graphics, Information System Management, Database Management System and Data

Engineering, Bioinformatics, Web Technology, Quantum Computing, and

e-Learning/Digital Content. From these literatures, 26 advising professors were

associated, thus forming the author database.

11

Chapter 5 System Prototype

The usages and screenshots of the implemented system are described in this

section. The custom designed main screen is as shown in Fig. 6.

Fig 6 Main screen

There are two search systems in DSpace one of them is Basic Search System

which is keyed in words by the user in the homepage. The other is Advanced Search

System which is chose research area, school year or teacher, or keyed in keywords by

the user and shown in Fig. 7.

A inquire in either search system would yield Search Result screen as shown in

Fig. 7, where items are listed in orders of keyword of the user input.

Fig 7 Search Result

Recommendations including Recommended Research Area, Recommended Papers,

are listed in the right side frameset, as shown in Fig. 8.

Result

12

Fig 8 Recommendations

Recommended authors are as shown in Fig. 9, where advising professors related to the

query are listed.

Fig 9 Recommended Authors

Recommended keywords can be seen in Fig. 10. In this example the query used was

ad hoc network. It can be seen that the generated related search terms are multicast

routing, proactive, routing, stay-alive time, and anycast.

Fig 10 Recommend keyword

Recommended Authors

Recommend keyword

Recommended

Research Area

Recommended

Papers

13

Statistical information can be browsed from the main screen. The Annual

Research Trends screen is shown in Fig. 11, which listed the amount of research

literatures in each research areas for 2001 and 2005. It could be seen that the featured

research area of the institution lies in the third and the sixth, which are Network

Computing and Information Security, both of which has a substantial amount of

papers annually. This helps a user to understand the features of the institution, which

could be useful to potential students, or department officers for self-evaluation.

Fig 11 Annual Research Trends

The research history of a single author can also be generated, which is called a

Faculty Profile Examination Page, as shown in Fig. 12. This is helpful to potential

students looking for mentorship, or to faculty who is looking for peers with similar

research interests.

Fig 12 Faculty Profile Examinations

14

Chapter 6 Experimental Results

To demonstrate the usability of the proposed system, a questionnaire survey was

conducted to get feedback from users in general, and the result is shown in Fig. 13.

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Fig 13 Level of Satisfaction

Among the 20 graduate students who have used the system, 30 % of them think

that the system is Very Helpful, 43.33% have chosen Helpful. Also, most (77%) users

would be glad if this prototype is insinuated into the actual FCU library DSpace

system.

15

Chapter 7 Conclusion

This work design and implements a recommender system for digital literature

retrieval. Algorithms for providing recommendations of relevant information are

proposed. The proposed system framework is designed and implemented onto an

existing DSpace-based departmental ETD system, and tested with existing papers.

The experimental result showed that in addition to providing effective filtering and

guideline services which help researcher greatly, statistical and trend information

gathered from documents can also be used for trend analysis or institutional

self-assessment.

16

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19

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21

Appendix

The following is shown the list of the modified file. We modified the code to

reach our goal of recommendation.

File Name Path Function

authors.jsp \jsp\browse Show all the authors.

items-by-author.jsp \jsp\browse Show all the items of author.

header-default.jsp \jsp\layout Show the image of header.

navbar-default.jsp \jsp\layout Show the left option.

results.jsp \jsp\search Show the search result.

review.jsp \jsp\submit Show the data of submission.

display-item.jsp \jsp\ Show the data of item.