Skip to main content
Springer Nature Link
Log in

Managing Corporate Portal Usage with Recommender Systems

  • Research Paper
  • Published:
Business & Information Systems Engineering Aims and scope Submit manuscript

Abstract

Corporate portals are supposed to support a company’s business model and to increase productivity of the employees. However, the productivity gain that can be achieved by corporate portals is often undermined because the users of the portal are not sufficiently informed about the portal’s capabilities. This is of particular concern for large corporate portals whose service portfolio is constantly evolving and to which new users are added frequently. In the article, we propose a recommender system for corporate portals in order to increase service awareness and usage. Following the design science methodology, a suitable recommender concept is developed and several implementation options are evaluated in a field experiment at one of Germany’s largest companies. It is found that the recommender system increases the number of newly visited services as well as the number of newly used services in the corporate portal by about 20 %.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

Notes

  1. More detailed descriptive statistics of this corporate portal are provided in Sect. 4.

  2. http://www.last.fm.

  3. http://www.netflix.com.

  4. http://www.facebook.com.

  5. For instance, different departments often have different wordings for comparable issues. This would need to be reflected in service descriptions and user preferences. Moreover, complex and specialized services often cannot be easily textually described.

  6. Each prediction p j for an item j is compared to the original rating r j with \(e_{\mathrm{MAE}} = \frac {1}{M} \sum_{j=1}^{M} |p_{j} - r_{j}|\), and \(e_{\mathrm{MSE}} = \sqrt{\frac {1}{M} \sum_{j=1}^{M} (p_{j} - r_{j})^{2}}\), respectively, where M is the number of predictions.

  7. The statistic is based on a total of 466 new users in the period from December 2009 to July 2010. Before this time period, a new user could not be unambiguously distinguished from a repeated user. New users that were later assigned to the treatment group are not considered.

  8. The ANOVA is equivalent to the well-known t-test if there is only one grouping variable that can take exactly two different values. Since we consider a full factorial design with three grouping variables (rating rule, user similarity, prediction algorithm), each of which can take two or more different values, the ANOVA is the more appropriate test. Similar to the t-test, the null hypothesis is that the mean prediction quality is the same across the different levels of the grouping variable. Under the null hypothesis, the ANOVA test statistic has an F-distribution. The ANOVA derives an F-value for each grouping variable, called the main effects, as well as an F-value for all possible combinations of the grouping variables, called the interaction effects. Given the degrees of freedom of the grouping variable (i.e., the number of values of the grouping variable minus one) and the residual degrees of freedom of the model (i.e., number of observations minus the sum of the degrees of freedom of the grouping variables minus one: here 89) , the p-value (significance level) can be computed.

  9. To obtain the Bonferroni adjusted p-value, the uncorrected p-value is multiplied by the total number of comparisons. If the answer exceeds 1.0, the corrected p-value is reported as 1.0.

  10. Here, the ANOVA coincides with the t-test. We refer to the ANOVA instead for consistency.

References

  • Adomavicius G, Tuzhilin A (2005) Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions. IEEE Transactions on Knowledge and Data Engineering 17(6):734–749

    Article  Google Scholar 

  • Arbin K, Essler U (2002) Emerging industrial eMarkets: the case of covisint in Europe. In: Proceedings of the 15th international electronic commerce conference, Bled, pp 279–292

    Google Scholar 

  • Ashenfelter O, Card D (1985) Using the longitudinal structure of earnings to estimate the effect of training programs. The Review of Economics and Statistics 67(4):648–660

    Article  Google Scholar 

  • Balabanović M, Shoham Y (1997) Fab: content-based, collaborative recommendation. Communications of the ACM 40(3):66–72

    Article  Google Scholar 

  • Baudisch P (1999) Joining collaborative and content-based filtering. In: Online proceedings of interacting with recommender systems, CHI ’99 workshop

    Google Scholar 

  • Bell RM, Koren Y (2007) Improved neighborhood-based collaborative filtering. In: KDDCup’07, San Jose

    Google Scholar 

  • Benbya H, Passiante G, Belbaly NA (2004) Corporate portal: a tool for knowledge management synchronization. International Journal of Information Management 24(3):201–220

    Article  Google Scholar 

  • Berkovsky S, Eytani Y, Manevitz LM (2007) Efficient collaborative filtering in content-addressable spaces. International Journal of Pattern Recognition and Artificial Intelligence (IJPRAI) 21(2):265–289

    Article  Google Scholar 

  • Breese JS, Heckerman D, Kadie C (1998) Empirical analysis of predictive algorithms for collaborative filtering. In: Proceedings of the 14th annual conference on uncertainty in artificial intelligence (UAI-98). Morgan Kaufmann, San Francisco, pp 43–52

    Google Scholar 

  • Buono P, Costabile MF, Guida S, Piccinno A (2002) Integrating user data and collaborative filtering in a web recommendation system. In: Reich S (ed) Hypermedia: openness, structural awareness, and adaptivity. Springer, Berlin

    Google Scholar 

  • Burke R, Felfernig A, Gker M (2011) Recommender systems: an overview. AI Magazine 32(3):13–18

    Google Scholar 

  • Calderón-Benavides ML, González-Caro CN, de Pérez-Alcázar JJ, Garciaz JC, Delgado J (2004) A comparison of several predictive algorithms for collaborative filtering on multi-valued ratings. In: Proceedings of the 2004 ACM symposium on applied computing (SAC ’04). ACM, New York, pp 1033–1039

    Chapter  Google Scholar 

  • Davydov MM (2001) Corporate portals and e-business integration. Emerging business technology series. McGraw-Hill, New York

    Google Scholar 

  • Delgado J, Nahoiro I (1999) Memory-based weighted-majority prediction for recommender systems. In: Workshop on recommender systems: algorithms and evaluation (SIGIR ’99), Berkeley

    Google Scholar 

  • Dias C (2001) Corporate portals: a literature review of a new concept in information management. International Journal of Information Management 21(4):269–287

    Article  Google Scholar 

  • Eckerson W (1999) Plumtree blossoms: new version fulfils enterprise portal requirements. Whitepaper. Boston

  • Ekstrand M, Riedl J, Konstan J (2011) Collaborative filtering recommender systems. Now Publishers, Boston

    Google Scholar 

  • Elsner H, Krämer J (2010) Einsatz von Recommender Systemen zum Management von Unternehmensportalen. In: Schumann M, Kolbe L, Breitner M, Friedrichs A (eds) Multikonferenz Wirtschaftsinformatik 2010. Universitätsverlag, Göttingen, pp 1819–1830

    Google Scholar 

  • Firestone JM (2000) Defining the enterprise information portal. http://www.tgc.com/dsstar/00/0822/102054.html. Accessed 2009-04-02

  • Girgensohn A, Shipman F, Chen F, Wilcox L (2010) DocuBrowse: faceted searching, browsing, and recommendations in an enterprise context. In: Proceedings of the 15th International conference on intelligent user interfaces. ACM, New York, pp 189–198

    Chapter  Google Scholar 

  • Goldberg D, Nichols D, Oki BM, Terry D (1992) Using collaborative filtering to weave an information tapestry. Communications of the ACM 35(12):61–70

    Article  Google Scholar 

  • Hazra TK (2002) Building enterprise portals: principles to practice. In: Proceedings of the 24th international conference on software engineering (ICSE 2002), pp 623–633

    Chapter  Google Scholar 

  • Herlocker JL, Konstan JA, Borchers A, Riedl J (1999) An algorithmic framework for performing collaborative filtering. In: Proceedings of the 22nd annual international ACM SIGIR conference on research and development in information retrieval (SIGIR ’99). ACM, New York, pp 230–237

    Chapter  Google Scholar 

  • Herlocker JL, Konstan JA, Terveen LG, Riedl JT (2004) Evaluating collaborative filtering recommender systems. ACM Transactions on Information Systems 22(1):5–53

    Article  Google Scholar 

  • Hevner A, March S, Park J, Ram S (2004) Design science in information systems research. MIS Quarterly 28(1):75–105

    Google Scholar 

  • Hill W, Stead L, Rosenstein M, Furnas G (1995) Recommending and evaluating choices in a virtual community of use. In: Proceedings of the SIGCHI conference on human factors in computing systems (CHI ’95). Addison-Wesley, New York, pp 194–201

    Google Scholar 

  • Jannach D, Zanker M, Felfernig A, Friedrich G (2010) Recommender systems: an introduction. Cambridge University Press, Cambridge

    Book  Google Scholar 

  • Knorr E (2004) The new enterprise portal: the browser-based portal is fast becoming the enterprise UI and the nexus for a new breed of integration and app dev. http://www.infoworld.com/d/developer-world/new-enterprise-portal-131. Accessed 2009-12-06

  • Konstan JA, Miller BN, Maltz D, Herlocker JL, Gordon LR, Riedl JT (1997) GroupLens: applying collaborative filtering to Usenet news. Communications of the ACM 40(3):77–87

    Article  Google Scholar 

  • Linden G, Smith B, York J (2003) Amazon.com recommendations: item-to-item collaborative filtering. IEEE Internet Computing 7(1):76–80

    Article  Google Scholar 

  • Malinowski J, Weitzel T, Keim T (2008) Decision support for team staffing: an automated relational recommendation approach. Decision Support Systems 45(3):429–447

    Article  Google Scholar 

  • Montaner M, López B, de La Rosa JL (2003) A taxonomy of recommender agents on the internet. Artificial Intelligence Review 19(4):285–330

    Article  Google Scholar 

  • Mulvenna MD, Anand SS, Buchner AG (2000) Personalization on the Net using web mining: introduction. Communications of the ACM 43(8):122–125

    Article  Google Scholar 

  • Murray G (1999) The portal is the desktop. Intraspect Inc, Los Altos

    Google Scholar 

  • Pazzani MJ (1999) A framework for collaborative, content-based and demographic filtering. Artificial Intelligence Review 13(5–6):393–408

    Article  Google Scholar 

  • Pazzani MJ, Billsus D (2007) Content-based recommendation systems. In: Brusilovsky P, Kobsa A, Nejdl W (eds) The adaptive web. Springer, Heidelberg, pp 325–341

    Chapter  Google Scholar 

  • Peffers K, Tuunanen T, Rothenberger M, Chatterjee S (2007) A design science research methodology for information systems research. Journal of Management Information Systems 24(3):45–77

    Article  Google Scholar 

  • Remus U (2007) Critical success factors for implementing enterprise portals: a comparison with ERP implementations. Business Process Management Journal 13(4):538–552

    Article  Google Scholar 

  • Resnick P, Iacovou N, Suchak M, Bergstrom P, Riedl J (1994) GroupLens: an open architecture for collaborative filtering of netnews. In: Proceedings of the 1994 ACM conference on computer supported cooperative work (CSCW ’94). ACM, New York, pp 175–186

    Chapter  Google Scholar 

  • Runte M (2000) Personalisierung im Internet: Individualisierte Angebote mit Collaborative Filtering. PhD thesis, Christian-Albrechts-Universität zu Kiel

  • Sarwar BM, Konstan JA, Borchers A, Herlocker JL, Miller B, Riedl J (1998) Using filtering agents to improve prediction quality in the GroupLens research collaborative filtering system. In: Proceedings of the 1998 ACM conference on computer supported cooperative work (CSCW ’98). ACM, New York, pp 345–354

    Chapter  Google Scholar 

  • Sarwar B, Karypis G, Konstan J, Reidl J (2001) Item-based collaborative filtering recommendation algorithms. In: Proceedings of the 10th international conference on world wide web (WWW ’01). ACM, New York, pp 285–295

    Google Scholar 

  • Schafer JB, Frankowski D, Herlocker J, Sen S (2007) Collaborative filtering recommender systems. In: Brusilovsky P, Kobsa A, Nejdl W (eds) The adaptive web. Springer, Heidelberg, pp 291–324

    Chapter  Google Scholar 

  • Shardanand U, Maes P (1995) Social information filtering: algorithms for automating “word of mouth”. In: Proceedings of the ACM conference on human factors in computing systems (CHI’95), pp 210–217

    Google Scholar 

  • Shilakes CC, Tylman J (1998) Enterprise information portals. Merrill Lynch in-depth report

  • Sullivan D (2003) Proven portals: best practices for planning, designing, and developing enterprise portals. Addison-Wesley, Boston

    Google Scholar 

  • Terra JC, Gordon C (2003) Realizing the promise of corporate portals: leveraging knowledge for business success. Butterworth-Heinemann, Amsterdam

    Google Scholar 

  • Vo HTK (2007) Engineering corporate portals. PhD thesis, Universität Karlsruhe (TH), Karlsruhe

  • Weiser M, Brown J (1996) Designing calm technology. PowerGrid Journal 1(1):75–85

    Google Scholar 

  • White C (2003) Is the portal dead? Information Management Magazine (July)

  • White M (2000) Enterprise information portals. The Electronic Library 18(5):354–362

    Article  Google Scholar 

  • Wisneski C, Ishii H, Dahley A, Gorbet M, Brave S, Ullmer B, Yarin P (1998) Ambient displays: turning architectural space into an interface between people and digital information. In: Streitz NA, Konomi S, Burkhardt H-J (eds) Cooperative buildings: integrating information, organization, and architecture. Springer, Heidelberg, pp 22–32

    Chapter  Google Scholar 

  • Zanker M, Jannach D, Gordea S, Jessenitschnig M (2007) Comparing recommendation strategies in a commercial context. IEEE Intelligent Systems 22(3):69–73

    Article  Google Scholar 

  • Zhen L, Huang G, Jiang Z (2009) Recommender system based on workflow. Decision Support Systems 48(1):237–245

    Article  Google Scholar 

  • Zhen L, Huang G, Jiang Z (2010) An inner-enterprise knowledge recommender system. Expert Systems with Applications 37(2):1703–1712

    Article  Google Scholar 

  • Ziegler CN, Lausen G (2004) Analyzing correlation between trust and user similarity in online communities. In: Proceedings of 2nd International conference on trust management, pp 251–265

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Institute of Information Systems and Management, Karlsruhe Institute of Technology, Englerstr. 14, 76131, Karlsruhe, Germany

    Helmuth Elsner &  Jan Krämer

Authors
  1. Helmuth Elsner
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jan Krämer
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jan Krämer.

Additional information

Accepted after two revisions by Prof. Dr. Bichler.

This article is also available in German in print and via http://www.wirtschaftsinformatik.de: Elsner H, Krämer J (2013) Nutzungsmanagement von Unternehmensportalen mithilfe von Empfehlungssystemen. WIRTSCHAFTSINFORMATIK. doi: 10.1007/s11576-013-0370-6.

Electronic Supplementary Material

Below is the link to the electronic supplementary material.

Managing Corporate Portal Usage with Recommender Systems (PDF 87 kB)

Rights and permissions

Reprints and permissions

About this article

Cite this article

Elsner, H., Krämer, J. Managing Corporate Portal Usage with Recommender Systems. Bus Inf Syst Eng 5, 213–225 (2013). https://doi.org/10.1007/s12599-013-0275-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12599-013-0275-3

Keywords