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
Themenseiten
Organisationspsychologie Projektmanagement Marketing Smart Manufacturing
Bücher
Zeitschriften
Weitere Formate
Podcasts Webinare Technik Webinare Wirtschaft Kongresse Veranstaltungskalender Awards
Jetzt Einzelzugang starten
Zugang für Unternehmen
Referenzkunden
MTZ-Fachkongress

Antriebe und Energiesysteme von morgen


Austausch von Automobil- und Energiewirtschaft

Am 14./15. Mai geht es in Chemnitz um die Mobilitätswende durch Elektro- und Wasserstofffahrzeuge.
Erweiterte Suche
Anmelden
nach oben
Multimedia Systems
Erschienen in: Multimedia Systems 6/2013

01.11.2013 | Regular Paper

Folkommender: a group recommender system based on a graph-based ranking algorithm

verfasst von: Heung-Nam Kim, Mark Bloess, Abdulmotaleb El Saddik

Erschienen in: Multimedia Systems | Ausgabe 6/2013

Einloggen

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

search-config
loading …

Abstract

With the rapid popularity of smart devices, users are easily and conveniently accessing rich multimedia content. Consequentially, the increasing need for recommender services, from both individual users and groups of users, has arisen. In this paper, we present a new graph-based approach to a recommender system, called Folkommender, that can make recommendations most notably to groups of users. From rating information, we first model a signed graph that contains both positive and negative links between users and items. On this graph we examine two distinct random walks to separately quantify the degree to which a group of users would like or dislike items. We then employ a differential ranking approach for tailoring recommendations to the group. Our empirical evaluations on two real-world datasets demonstrate that the proposed group recommendation method performs better than existing alternatives. We also demonstrate the feasibility of Folkommender for smartphones.
Vorheriger Artikel User interest modeling for social TV-recommender systems based on audiovisual consumption
Nächster Artikel Automatic and personalized recommendation of TV program contents using sequential pattern mining for smart TV user interaction

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
Fußnoten
1
A generally accepted value for d is 0.15.
 
2
The dataset can be downloaded from http://​www.​grouplens.​org/​node/​73.
 
3
In this user study, α was set to 0.7.
 
Literatur
1.
Adomavicius, G., Tuzhilin, A.: Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions. IEEE Trans. Knowl. Data Eng. 17(6), 734–749 (2005)CrossRef
2.
Amer-Yahia, S., Roy, S.B., Chawlat, A., Das, G., Yu, C.: Group recommendation: semantics and efficiency. In: Proceedings of the 35th International Conference on Very Large Data Bases, pp. 754–765 (2009)
3.
Baltrunas, L., Makcinskas, T., Ricci, F.: Group recommendations with rank aggregation and collaborative filtering. In: Proceedings of the 4th ACM Conference on Recommender Systems, pp. 119–126 (2010)
4.
5.
Berkovsky, S., Freyne, J.: Group-based recipe recommendations: analysis of data aggregation strategies. In: Proceedings of the 4th ACM Conference on Recommender Systems, pp. 111–118 (2010)
6.
Breese, J.S., Heckerman, D., Kadie, C.: Empirical analysis of predictive algorithms for collaborative filtering, In: Proceedings of the 14th Annual Conference on Uncertainty in Artificial Intelligence, pp. 43–52 (1998)
7.
Brin, S., Page, L., Motwami, R., Winograd, T.: The PageRank citation ranking: Bringing order to the Web. Stanford University, Technical Report (1999)
8.
de Campos, L.M., Fernández-Luna, J.M., Huete, J.F., Rueda-Morales, M.A.: Managing uncertainty in group recommending processes. User Model. User Adapt. Interact. 19(3), 207–242 (2009)CrossRef
9.
Deshpande, M., Karypis, G.: Item-based top-n recommendation algorithms. ACM Trans. Inf. Syst. 22(1), 143–177 (2004)CrossRef
10.
Gartrell, M., Xing, X., Lv, Q., Beach, A., Han, R., Mishra, S., Seada, K.: Enhancing group recommendation by incorporating social relationship interactions. In: Proceedings of the 16th ACM International Conference on Supporting Group Work, pp. 97–106 (2010)
11.
Herlocker, J.L., Konstan, J.A., Terveen, L.G., Riedl, J.T.: Evaluating collaborative filtering recommender systems. ACM Trans. Inf. Syst. 22(1), 5–53 (2004)CrossRef
12.
Jameson, A., Smyth, B.: Recommendation to groups. In: Brusilovsky, P., Kobsa, A., Nejdl W. (eds.) The Adaptive Web. Lecture Notes in Computer Science 4321, pp. 596–627. Springer, Berlin (2007)
13.
Jeh, G., Widom, J.: Scaling personalized Web search. In: Proceedings of the 12th International Conference on World Wide Web, pp. 271–279 (2003)
14.
Kim, H.K., Oh, H.Y., Gu, J.C., Kim, J.K.: Commenders: a recommendation procedure for online book communities. Electron. Commer. Res. Appl. 10(5), 501–509 (2011)CrossRef
15.
Konstan, J.A., Riedl, J.: Recommender systems: from algorithms to user experience. User Model. User Adapt. Interact. 22, 101–123 (2012)CrossRef
16.
Langville, A.N., Meyer, C.D.: A survey of eigenvector methods for Web information retrieval. SIAM Rev. 47(1), 135–161 (2005)MathSciNetCrossRefMATH
17.
Masthoff, J.: Group modeling: selecting a sequence of television items to suit a group of viewers. User Model. User Adapt. Interact. 14(1), 37–85 (2004)CrossRef
18.
McCarthy, J.F., Anagnost, T.D.: MUSICFX: an arbiter of group preferences for computer supported collaborative workouts. In: Proceedings of the 1998 ACM Conference on Computer Supported Cooperative Work, pp. 363–372 (1998)
19.
O’Connor, M., Cosley, D., Konstan, J.A., Riedl, J.: PolyLens: a recommender system for groups of users. In: Proceedings of the 7th Conference on European Conference on Computer Supported Cooperative Work, pp. 199–218 (2001)
20.
Sarwar, B., Karypis, G., Konstan, J., Riedl, J.: Item-based collaborative filtering recommendation algorithms. In: Proceedings of the 10th international conference on World Wide Web, pp. 285–295 (2001)
21.
Senot, C., Kostadinov, D., Bouzid, M., Picault, J., Aghasaryan, A., Bernier, C.: Analysis of strategies for building group profiles. In: Proceedings of the 18th International Conference on User Modeling, Adaptation, and Personalization, pp. 40–51 (2010)
22.
Sotelo, R., Blanco-Fernandez, Y., Lopez-Nores, M., Gil-Solla, A., Pazos-arias, J.: TV program recommendation for groups based on multidimensional TV-Anytime classifications. IEEE Trans. Consum. Electron. 55(1), 248–256 (2009)CrossRef
23.
Tong, T., Faloutsos, C., Pan, J.-Y.: Fast random walk with restart and its applications. In: Proceedings of the 6th International Conference on Data Mining, pp. 613–622 (2006)
24.
Yu, Z., Zhou, X., Hao, Y., Gu, J.: TV program recommendation for multiple viewers based on user profile merging. User Model. User Adapt. Interact. 16(1), 63–82 (2006)CrossRef
25.
Yu, Z., Zhou, X., Zhou, L., Du, K.: A hybrid similarity measure of contents for TV personalization. Multimed. Syst. 16, 231–241 (2010)CrossRef
26.
Zhao, X., Yuan, J., Wang, M., Li, G., Hong, R., Li, Z., Chua, T.-S.: Video recommendation over multiple information sources. Multimed. Syst. (2012). doi:10.​1007/​s00530-012-0267-z
Metadaten
Titel
Folkommender: a group recommender system based on a graph-based ranking algorithm
verfasst von
Heung-Nam Kim
Mark Bloess
Abdulmotaleb El Saddik
Publikationsdatum
01.11.2013
Verlag
Springer Berlin Heidelberg
Erschienen in
Multimedia Systems / Ausgabe 6/2013
Print ISSN: 0942-4962
Elektronische ISSN: 1432-1882
DOI
https://doi.org/10.1007/s00530-012-0298-5

Weitere Artikel der Ausgabe 6/2013

Multimedia Systems 6/2013 Zur Ausgabe

Regular Paper

Twitter analysis algorithms for Intelligence Circulation System

Regular Paper

Automatic and personalized recommendation of TV program contents using sequential pattern mining for smart TV user interaction

Regular Paper

A recommender system for the TV on the web: integrating unrated reviews and movie ratings

Original Article

Accessing movies based on emotional impact

Regular Paper

User interest modeling for social TV-recommender systems based on audiovisual consumption

Editorial

Special Issue on Social Recommendation and Delivery Systems for Video and TV Content

Neuer Inhalt

    Bildnachweise