Skip to main content
Fachgebiete
Automobil + Motoren Bauwesen + Immobilien Business IT + Informatik Elektrotechnik + Elektronik Energie + Nachhaltigkeit Finance + Banking Management + Führung Marketing + Vertrieb Maschinenbau + Werkstoffe Versicherung + Risiko
DE
EN
Bücher
Zeitschriften
Themenseiten
Organisationspsychologie Projektmanagement Marketing Smart Manufacturing
Weitere Formate
Podcasts Webinare Technik Webinare Wirtschaft Kongresse Veranstaltungskalender Awards
Jetzt Einzelzugang starten
Zugang für Unternehmen
Referenzkunden
SLX-Digitalkonferenz: Zukunftswerkstatt 2024

Mehr Umsatz mit Top-Kontakten

Am 7. Mai erfahren Sie, wie Vertriebsteams Leadgenerierung, Leadnurturing und Leadstrategien effizient steuern können.
Erweiterte Suche
Anmelden
nach oben
Erschienen in:
An Engineering Approach for the Design of Hybrid Modelling Methods Kapitel lesen Erstes Kapitel lesen

2013 | OriginalPaper | Buchkapitel

Bringing Diversity to Recommendation Lists – An Analysis of the Placement of Diverse Items

verfasst von : Mouzhi Ge, Dietmar Jannach, Fatih Gedikli

Erschienen in: Enterprise Information Systems

Verlag: Springer Berlin Heidelberg

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config
loading …

Abstract

The core task of a recommender system is to provide users with a ranked list of recommended items. In many cases, the ranking is based one a recommendation score representing the estimated degree to which the users will like them. Up to now research specifically focused on the accuracy of recommender algorithms in predicting the relevance of items for a given user. However, researchers agree that there are other factors than prediction accuracy which can have a significant effect on the overall quality of a recommender system. Therefore, additional and complementary metrics, including diversity, novelty, transparency and serendipity should be used to evaluate the quality of recommender systems.
In this paper we will focus on diversity which has been more widely discussed in recent research and is often considered to be a factor which is equally important as accuracy. In particular we address the question of how to place diverse items in a recommendation list and measure the user-perceived level of diversity. Differently placing the diverse items can affect perceived diversity and the level of serendipity. Furthermore, the results of our analysis show that including diverse items in a recommendation list can both increase and sometimes even decrease the perceived diversity and that the effect depends on how the diverse items are arranged.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

Jetzt informieren
Vorheriges Kapitel Large-Scale Enterprise Systems: Changes and Impacts
Nächstes Kapitel Time Efficiency of Point-of-Sale Payment Methods: Empirical Results for Cash, Cards and Mobile Payments
Literatur
1.
Adomavicius, G., Kwon, Y.: Improving aggregate recommendation diversity using ranking-based techniques. IEEE Trans. Knowl. Data Eng. 99, 1–15 (2011)
2.
Adomavicius, G., Kwon, Y.: Maximizing aggregate recommendation diversity: a graph-theoretic approach. In: Proceedings of Workshop on Novelty and Diversity in Recommender Systems, Chicago, Illinois, USA, pp. 3–10 (2011b)
3.
Adamopoulos, P., Tuzhilin, A.: On unexpectedness in recommender systems: or how to expect the unexpected. In: Proceedings of Workshop on Novelty and Diversity in Recommender Systems, Chicago, Illinois, USA (2011)
4.
Castells, P., Vargas, S., Wang, J.: Novelty and diversity metrics for recommender systems: choice, discovery and relevance. In: Proceedings of International Workshop on Diversity in Document Retrieval, Dublin, Ireland, pp. 29–37 (2011)
5.
Clarke, C. L. A., Craswell, N., Soboroff, I., Ashkan, A.: A comparative analysis of cascade measures for novelty and diversity. In: Proceedings of the Fourth ACM International Conference on Web Search and Data Mining, Hong Kong, China, pp. 75–84 (2011)
6.
Dias, M. B., Locher, D., Li, M., El-Deredy, W., Lisboa, P. J.: The value of personalised recommender systems to e-business: a case study. In: Proceedings of the 2008 ACM Conference on Recommender Systems, Lausanne, Switzerland, pp. 291–294 (2008)
7.
Fleder, D., Hosanagar, K.: Recommender systems and their impact on sales diversity. In: Proceedings of the 8th ACM Conference on Electronic Commerce, San Diego, CA, USA, pp. 192–199 (2007)
8.
Ge, M., Delgado-Battenfeld, C., Jannach, D.: Beyond accuracy: evaluating recommender systems by coverage and serendipity. In: Proceedings of the Fourth ACM Conference on Recommender Systems, New York, pp. 257–260 (2010)
9.
Gedikli, F., Ge, M., Jannach, D.: Understanding recommendations by reading the clouds. In: Huemer, C., Setzer, T. (eds.) EC-Web 2011. LNBIP, vol. 85, pp. 196–208. Springer, Heidelberg (2011)
10.
Herlocker, L., Konstan, J., Terveen, L., Riedl, J.: Evaluating collaborative filtering recommender systems. ACM Trans. Info. Syst. 22(1), 5–53 (2004)CrossRef
11.
Jannach, D., Hegelich, K.: A case study on the effectiveness of recommendations in the mobile Internet, pp. 205–208. ACM Conference on Recommender Systems, New York (2009)
12.
Jannach, D., Zanker, M., Felfernig, A., Friedrich G.: Recommender Systems: An Introduction. Cambridge University Press (2010)CrossRef
13.
Jannach, D., Zanker, M., Ge, M., Gröning, M.: Recommender Systems in Computer Science and Information Systems – A Landscape of Research. In: Huemer, C., Lops, P. (eds.) EC-Web 2012. LNBIP, vol. 123, pp. 76–87. Springer, Heidelberg (2012)
14.
Lathia, N., Hailes, S., Capra, L., Amatriain, X.: Temporal diversity in recommender systems. In: Proceedings of the 33rd International ACM SIGIR Conference on Research and Development in Information Retrieval, Geneva, Switzerland, pp. 210–217 (2010)
15.
McNee, S., Riedl, J., Konstan, J.: Being accurate is not enough: how accuracy metrics have hurt recommender systems. In: Proceedings of the ACM SIGCHI Conference on Human Factors in Computing Systems. Montréal, Canada, pp. 1097–1101 (2006)
16.
Smyth, B., McClave, P.: Similarity vs. Diversity. In: Aha, D.W., Watson, I. (eds.) ICCBR 2001. LNCS (LNAI), vol. 2080, pp. 347–361. Springer, Heidelberg (2001)
17.
Sakai, T.: Challenges in diversity evaluation. In: Proceedings of International Workshop on Diversity in Document Retrieval. Dublin, Ireland, pp. 1–7 (2011)
18.
Zhou, T., Kuscsika, Z., Liua, J., Medoa, M., Wakelinga, J., Zhang, Y.: Solving the apparent diversity-accuracy dilemma of recommender systems. In: Proceedings of National Academy of Sciences of the USA, vol. 107 (10), pp. 4511–4515 (2010)
19.
Zhang, M., Hurley, N.: Avoiding monotony: improving the diversity of recommendation lists. In: Proceedings of the 2nd ACM Conference on Recommender Systems, Lausanne, Switzerland, pp. 123–130 (2008)
20.
Ziegler, C., McNee, S., Konstan, J., Lausen, G.: Improving recommendation lists through topic diversification. In: Proceedings of the 14th World Wide Web Conference. Chiba, Japan, pp. 22–32 (2005)
21.
Zanker, M., Bricman, M., Gordea, S., Jannach, D., Jessenitschnig, M.: Persuasive online-selling in quality and taste domains. In: Bauknecht, K., Pröll, B., Werthner, H. (eds.) EC-Web 2006. LNCS, vol. 4082, pp. 51–60. Springer, Heidelberg (2006)
Metadaten
Titel
Bringing Diversity to Recommendation Lists – An Analysis of the Placement of Diverse Items
verfasst von
Mouzhi Ge
Dietmar Jannach
Fatih Gedikli
Copyright-Jahr
2013
Verlag
Springer Berlin Heidelberg
Buch
Enterprise Information Systems

Print ISBN: 978-3-642-40653-9

Electronic ISBN: 978-3-642-40654-6

Copyright-Jahr: 2013

DOI
https://doi.org/10.1007/978-3-642-40654-6_18

Premium Partner

    Bildnachweise