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Computer Supported Cooperative Work (CSCW)

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01.10.2007

Expert Recommender: Designing for a Network Organization

verfasst von: Tim Reichling, Michael Veith, Volker Wulf

Erschienen in: Computer Supported Cooperative Work (CSCW) | Ausgabe 4-5/2007

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Abstract

Recent knowledge management initiatives focus on expertise sharing within formal organizational units and informal communities of practice. Expert recommender systems seem to be a promising tool in support of these initiatives. This paper presents experiences in designing an expert recommender system for a knowledge-intensive organization, namely the National Industry Association (NIA). Field study results provide a set of specific design requirements. Based on these requirements, we have designed an expert recommender system which is integrated into the specific software infrastructure of the organizational setting. The organizational setting is, as we will show, specific for historical, political, and economic reasons. These particularities influence the employees’ organizational and (inter-)personal needs within this setting. The paper connects empirical findings of a long-term case study with design experiences of an expertise recommender system.

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“NIA” is actually not the real name but a pseudonym.

Member-centered communication spaces may include some topic-centred functionality. However, their design assumes a rather stable group of users basically known to each other.

In the following we will use the term “sector” in the sense of branch of industry, whereas “section” will refer to NIA’s sectoral departments.

During the past 10 years additional organizations appeared which offer networking and knowledge services to – mainly small and medium sized - companies. NIA, however, is the only officially recognized industry representation in these branches. Thus, it can provide exclusive links to political committees and policy-makers.

As an example, some agricultural companies need support from the ‘agricultural technology’ and from the ‘pumps and system’ section since many agricultural machines and tractors are additionally equipped with systems and machines that are developed by member companies of other sections.

About 20 users participated in evaluating the expertise recommender. We conducted another 21 more interviews and workplace observations with users (some of them twice at different stages) during the evaluation period. These participants overlapped partly with those of the early empirical study.

This importance is defined by two major criteria: First, the actual size of a member company, as the membership subscription is based on size, and second, by micro-political considerations regarding the engagement of a member company within NIA’s policies.

These experts may work in certain horizontal sections (e.g. the agricultural section) or in vertical sections (e.g. the IT or standardization department).

It becomes obvious from the empirical findings that not only high level expert knowledge is requested. Even lower degrees and different aspects of ‘expertise’ (namely interests, experiences, activities or abilities) belong to the requested properties (cf. Hinds and Pfeffer 2003 or Ackerman et al. 2003b; Pipek and Wulf 2003).

We believed that such a design approach would also improve the quality of the keyword vector since we included only relevant documents for matching.

The Web Service interface allows only for expertise recommendations. Since local creation of the users’ keyword profiles is a central part of our approach and a Web Service would require uploading entire files, the client system still needs to be installed locally in order to generate and eventually edit keyword profiles before uploading them.

MS Excel files are widely used as well at NIA. However, Excel files are not yet supported since we assumed these files to include numbers rather than meaningful keywords. Our latest investigations show that this is actually not true for NIA since some Excel sheets appeared to include meaningful keywords. Hence, support for Excel files will be included in the next version of the system.

Partial matches (truncations) are considered as matches of ‘lower quality’. Accordingly, in cases of partial matches a smaller value is added to the matching result, due to the number of matching letters.

TF/IDF stands for term frequency inversed document frequency. Compared to simple term frequency, this method provides a better understanding of the keyword’s importance in the context of the other user profiles (cf. Salton and McGill 1983).

Presenting this method in detail would exceed the scope of this paper. Therefore, we refer to Berry et al. (1995) for a detailed description.

The current implementation offers information on the quality of the automatically generated recommendations. However, in case member companies deploy the system to a large extend, we may need to reconsider the display of too many details at this point. For example, NIA may not like to let its member companies know that the best match with regard to a ‘hot topic’ has a score of only 26%.

NIA’s employees are used to work with these sets of data and do not want to maintain additional information.

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Titel: Expert Recommender: Designing for a Network Organization
verfasst von: Tim Reichling
Michael Veith
Volker Wulf
Publikationsdatum: 01.10.2007
Verlag: Springer Netherlands
Erschienen in: Computer Supported Cooperative Work (CSCW) / Ausgabe 4-5/2007
Print ISSN: 0925-9724
Elektronische ISSN: 1573-7551
DOI: https://doi.org/10.1007/s10606-007-9055-2

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