A multi-disciplinar recommender system to advice research resources in University Digital Libraries
Introduction
In the last years the new concept of digital library is growing. Digital libraries are information collections that have associated services delivered to user communities using a variety of technologies. The information collections can be scientific, business or personal data, and can be represented as digital text, image, audio, video, or other media. This information can be displayed on the digitalized paper or born digital material and the services offered on such information can be varied and can be offered to individuals or user communities (Callan et al., 2003, Gonçalves et al., 2004, Renda and Straccia, 2005).
Digital libraries are the logical extensions of physical libraries in the electronic information society. These extensions amplify existing resources and services. As such, digital libraries offer new levels of access to broader audiences of users and new opportunities for the library. In practice, a digital library makes its contents and services remotely accessible through networks such as the Web or limited-access intranets (Marchionini, 2009).
The digital libraries are composed of human resources (staff) that take over handle and enable the users to access the documents that are more interesting for them, taking into account their needs or areas of interest. The library staff searches, evaluates, selects, catalogues, classifies, preserves and schedules the digital documents access (Gonçalves et al., 2004). Some of the main digital libraries functions are the following:
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To evaluate and select digital materials to add in its repository.
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To preserve the security and conservation of the materials.
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To describe and index the new digital materials (catalogue and classify).
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To deliver users the material stored in the library.
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Other managerial tasks.
Libraries offer different types of references and referral services (e.g., ready reference, exhaustive search, and selective dissemination of information), instructional services (e.g., bibliographic instruction and database searching), added value services (e.g., bibliography preparation, and language translation) and promotional services (e.g., literacy and freedom of expression). As digital libraries become commonplace and as their contents and services become more varied, the users expect more sophisticated services from their digital libraries (Callan et al., 2003, Gonçalves et al., 2004, Renda and Straccia, 2005).
A service that is particularly important is the selective dissemination of information or filtering (Morales del Castillo et al., 2009, Morales del Castillo et al., in press). Users develop profiles that reveals their areas of interest and as new materials are added to the collection, they are compared to the profiles and relevant items are sent to the users (Marchionini, 2009).
One interesting extension of this concept is to use the connectivity inherent in digital libraries to support collaborative filtering, where users rate or add value to information objects and these ratings are shared with a large community, so that popular items can be easily located or people can search for objects found useful by others with similar profiles (Hanani et al., 2001, Marchionini, 2009, Reisnick and Varian, 1997).
Digital libraries have been applied in many contexts but in this paper we focus on an academic environment. University Digital Libraries (UDLs) provide information resources and services to students, faculty and staff in an environment that supports learning, teaching and research (Chao, 2002).
In this paper we propose a fuzzy linguistic recommender system to achieve major advances in the activities of UDL in order to improve their performance. The system is oriented to researchers and it recommends two types of resources: in the first place, specialized resources of the user research area, and in the second place, complementary resources in order to include resources of related areas that could be interesting to discover collaboration possibilities with other researchers and to form multi-disciplinar groups. As in (Porcel, López-Herrera, & Herrera-Viedma, 2009) we combine a recommender system, to filter out the information, with a multi-granular Fuzzy Linguistic Modeling (FLM), to represent and handle flexible information by means of linguistic labels (Chang et al., 2007, Chen and Ben-Arieh, 2006, Herrera and Martínez, 2001, Herrera-Viedma et al., 2003, Herrera-Viedma et al., 2005, Herrera et al., 2008).
The paper is structured as follows. Section 2 revises some preliminaries, i.e., the concept and main aspects about recommender systems and the approaches of FLM that we use to the system design, the 2-tuple FLM and the multi-granular FLM. In Section 3 we present a multi-disciplinar fuzzy linguistic recommender systems to advice research resources in UDL. Section 4 reports the system evaluation and some experimental results. Finally, some concluding remarks are pointed out.
Section snippets
Recommender systems
Recommender systems could be defined as systems that produce individualized recommendations as output or has the effect of guiding the user in a personalized way to interesting or useful objects in a large space of possible options (Burke, 2002). They are becoming popular tools for reducing information overload and for improving the sales in e-commerce web sites (Burke, 2007, Cao and Li, 2007, Hsu, 2008, Reisnick and Varian, 1997).
It is a research area that offers tools for discriminating
A multi-disciplinar recommender system to advice research resources in UDL
In this section we present a fuzzy linguistic recommender system designed using a hybrid approach and assuming a multi-granular FLM. This system is applied to advice users on the best research resources that could satisfy their information needs in a UDL. Moreover, the system recommends complementary resources that could be used by the users to meet other researchers of related areas with the aim to discover collaboration possibilities and so, to form multi-disciplinar groups. In this way, it
Experiment and evaluation
In this section we present the evaluation of the proposed system. The main focus in evaluating the system is to determine if it fulfills the proposed objectives, that is, the recommended information is useful and interesting for the users. At the moment, we have implemented a trial version, in which the system works only with a few researchers. In a later version we will include the system in a UDL.
To evaluate this trial version we have designed experiments in which the system is used to
Conclusions
Internet access has resulted in digital libraries that are increasingly used by diverse communities for diverse purposes, and in which sharing and collaboration have become important social elements. Users of UDL need tools to assist them in their processes of information gathering because of the large amount of information available on these systems. We have presented a multi-disciplinar fuzzy linguistic recommender system to spread research resources in UDL. The proposed system is oriented to
Acknowledgements
This paper has been developed with the financing of SAINFOWEB Project (TIC00602) and FEDER funds in FUZZYLING Project (TIN2007-61079), PETRI project (PET2007-0460), and Project of Ministry of Public Works (90/07).
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