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
Electronic Commerce Research
Erschienen in: Electronic Commerce Research 4/2020

24.10.2018

Situation awareness for recommender systems

verfasst von: Christian Richthammer, Günther Pernul

Erschienen in: Electronic Commerce Research | Ausgabe 4/2020

Einloggen

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

search-config
loading …

Abstract

One major shortcoming of traditional recommender systems is their inability to adjust to users’ short-term preferences resulting from varying situation-specific factors. To address this, we propose the notion of situation-aware recommender systems, which are supposed to autonomously determine the users’ current situation based on a multitude of contextual side information and generate truly personalized recommendations. In particular, we develop a situation awareness model for recommender systems, include it in a situation-aware recommendation process, and derive generic design steps for the design of situation-aware recommender systems. The feasibility of these concepts is demonstrated by directly employing them for the development and implementation of a music recommender system for everyday situations. Moreover, their meaningfulness is shown by means of an empirical user study. The outcomes of the evaluation indicate a significant increase in user satisfaction compared to traditional (i.e. non-situation-aware) recommendations.
Vorheriger Artikel The value of backers’ word-of-mouth in crowdfunding projects filtering: an empirical investigation
Nächster Artikel Exploring contextual factors from consumer reviews affecting movie sales: an opinion mining approach

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 "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

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
Fußnoten
1
Responding was optional for all personal data as well as for all contextual conditions.
 
9
Creating a temporary private playlist is necessary because the Spotify API does not support queuing songs.
 
10
Providing personal data was optional.
 
Literatur
1.
Adams, M. J., Tenney, Y. J., & Pew, R. W. (1995). Situation awareness and the cognitive management of complex systems. Human Factors: The Journal of the Human Factors and Ergonomics Society, 37(1), 85–104.
2.
Adomavicius, G., Mobasher, B., Ricci, F., & Tuzhilin, A. (2011). Context-aware recommender systems. AI Magazine, 32(3), 67–80.
3.
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.
4.
Aggarwal, C. C. (2016). Context-sensitive recommender systems. In C. C. Aggarwal (Ed.), Recommender systems: The textbook (pp. 255–281). Cham: Springer International Publishing.
5.
Asoh, H., Motomura, Y., & Ono, C. (2010). An analysis of differences between preferences in real and supposed contexts. In Proceedings of the 2nd workshop on context-aware recommender systems (CARS).
6.
Balabanović, M., & Shoham, Y. (1997). Fab: Content-based, collaborative recommendation. Communications of the ACM, 40(3), 66–72.
7.
Baltrunas, L. (2011). Context-aware collaborative filtering recommender systems. Ph.D. thesis, Free University of Bozen-Bolzano, Bozen, Italy.
8.
Baltrunas, L., & Amatriain, X. (2009). Towards time-dependant recommendation based on implicit feedback. In Proceedings of the 1st Workshop on context-aware recommender systems (CARS).
9.
Baltrunas, L., Ludwig, B., Peer, S., & Ricci, F. (2012). Context relevance assessment and exploitation in mobile recommender systems. Personal and Ubiquitous Computing, 16(5), 507–526.
10.
Bauer, C., & Novotny, A. (2017). A consolidated view of context for intelligent systems. Journal of Ambient Intelligence and Smart Environments, 9(4), 377–393.
11.
Bazire, M., & Brézillon, P. (2005). Understanding context before using it. In Proceedings of the international and interdisciplinary conference on modeling and using context (CONTEXT) (pp. 29–40).
12.
Burke, R. (2000). Knowledge-based recommender systems. In A. Kent (Ed.), Encyclopedia of library and information science: volume 69—supplement 32 (pp. 180–200). New York, NY: Marcel Dekker Inc.
13.
Burke, R. (2002). Hybrid recommender systems: Survey and experiments. User Modeling and User-Adapted Interaction, 12(4), 331–370.
14.
Burke, R., Felfernig, A., & Göker, M. H. (2011). Recommender systems: An overview. AI Magazine, 32(3), 13–18.
15.
Celma, O., & Lamere, P. (2011). If you like radiohead, you might like this article. AI Magazine, 32(3), 57–66.
16.
Chen, C. M., Tsai, M. F., Lin, Y. C., & Yang, Y. H. (2016). Query-based music recommendations via preference embedding. In Proceedings of the 10th ACM conference on recommender systems (RecSys) (pp. 79–82).
17.
Clapham, C., & Nicholson, J. (2014). The concise oxford dictionary of mathematics (5th ed.). Oxford: Oxford University Press.
18.
Cohen, J. (1992). A power primer. Psychological Bulletin, 112(1), 155–159.
19.
Dey, A. K. (2001). Understanding and using context. Personal and Ubiquitous Computing, 5(1), 4–7.
20.
Dourish, P. (2004). What we talk about when we talk about context. Personal and Ubiquitous Computing, 8(1), 19–30.
21.
Endsley, M. R. (1988). Situation awareness global assessment technique (SAGAT). In Proceedings of the IEEE 1988 national aerospace and electronics conference (NAECON) (pp. 789–795).
22.
Endsley, M. R. (1993). A survey of situation awareness requirements in air-to-air combat fighters. The International Journal of Aviation Psychology, 3(2), 157–168.
23.
Endsley, M. R. (1995). Toward a theory of situation awareness in dynamic systems. Human Factors: The Journal of the Human Factors and Ergonomics Society, 37(1), 32–64.
24.
Feng, Y. H., Teng, T. H., & Tan, A. H. (2009). Modelling situation awareness for context-aware decision support. Expert Systems with Applications, 36(1), 455–463.
25.
Fritz, C. O., Morris, P. E., & Richler, J. J. (2012). Effect size estimates: current use, calculations, and interpretation. Journal of Experimental Psychology: General, 141(1), 2–18.
26.
Herbig, P. A., & Kramer, H. (1994). The effect of information overload on the innovation choice process. Journal of Consumer Marketing, 11(2), 45–54.
27.
Hevner, A. R., March, S. T., Park, J., & Ram, S. (2004). Design science in information systems research. MIS Quarterly, 28(1), 75–105.
28.
Kaminskas, M., & Ricci, F. (2012). Contextual music information retrieval and recommendation: State of the art and challenges. Computer Science Review, 6(2–3), 89–119.
29.
Kells, L. M., & Kern, W. F. (1935). Plane and spherical trigonometry. New York: Maple Press Company.
30.
Levy, Y., & Ellis, T. J. (2006). A systems approach to conduct an effective literature review in support of information systems research. Informing Science: The International Journal of an Emerging Transdiscipline, 9, 181–212.
31.
Liu, N. H. (2013). Comparison of content-based music recommendation using different distance estimation methods. Applied Intelligence, 38(2), 160–174.
32.
Melville, P., Mooney, R., & Nagarajan, R. (2002). Content-boosted collaborative filtering for improved recommendations. In Proceedings of the 18th national conference on artificial intelligence (AAAI) (pp. 187–192).
33.
Niu, L., Lu, J., & Zhang, G. (2009). Managerial cognition. In Kacprzyk (Ed.), Cognition-driven decision support for business Intelligence, studies in computational intelligence (Vol. 238, pp. 31–37). Berlin: Springer.
34.
Odic, A., Tkalcic, M., Tasic, J. F., & Kosir, A. (2012). Relevant context in a movie recommender system: Users’ opinion versus statistical detection. In Proceedings of the 4th ACM RecSys workshop on context-aware recommender systems (CARS).
35.
Panniello, U., Tuzhilin, A., & Gorgoglione, M. (2014). Comparing context-aware recommender systems in terms of accuracy and diversity. User Modeling and User-Adapted Interaction, 24(1–2), 35–65.
36.
Peffers, K., Tuunanen, T., Rothenberger, M. A., & Chatterjee, S. (2007). A design science research methodology for information systems research. Journal of Management Information Systems, 24(3), 45–77.
37.
Salas, E., Prince, C., Baker, D. P., & Shrestha, L. (1995). Situation awareness in team performance: Implications for measurement and training. Human Factors: The Journal of the Human Factors and Ergonomics Society, 37(1), 123–136.
38.
Schedl, M. (2013). Ameliorating music recommendation: Integrating music content, music context, and user context for improved music retrieval and recommendation. In Proceeding of the international conference on advances in mobile computing and multimedia (MoMM) (pp. 3–9).
39.
Schedl, M., & Bauer, C. (2018). An analysis of global and regional mainstreaminess for personalized music recommender systems. Journal of Mobile Multimedia, 14(1), 95–112.
40.
Stanners, M., & French, H. T. (2005). An empirical study of the relationship between situation awareness and decision making. Tech. Rep. DSTO-TR-1687, Defence Science and Technology Organisation, Department of Defence, Australian Government.
41.
Stanton, N. A., Stewart, R., Harris, D., Houghton, R. J., Baber, C., McMaster, R., et al. (2006). Distributed situation awareness in dynamic systems: Theoretical development and application of an ergonomics methodology. Ergonomics, 49(12–13), 1288–1311.
42.
Tzanetakis, G., & Cook, P. (2002). Musical genre classification of audio signal. IEEE Transactions on Speech and Audio Processing, 10(5), 293–302.
43.
Webster, J., & Watson, R. T. (2002). Analyzing the past to prepare for the future: Writing a literature review. MIS Quarterly, 26(2), 13–23.
44.
Zheng, Y., Burke, R., & Mobasher, B. (2014). Splitting approaches for context-aware recommendation. In Proceedings of the 29th annual ACM symposium on applied computing (SAC) (pp. 274–279).
Metadaten
Titel
Situation awareness for recommender systems
verfasst von
Christian Richthammer
Günther Pernul
Publikationsdatum
24.10.2018
Verlag
Springer US
Erschienen in
Electronic Commerce Research / Ausgabe 4/2020
Print ISSN: 1389-5753
Elektronische ISSN: 1572-9362
DOI
https://doi.org/10.1007/s10660-018-9321-z

Weitere Artikel der Ausgabe 4/2020

Electronic Commerce Research 4/2020 Zur Ausgabe

OriginalPaper

Smartphone use and income growth in rural China: empirical results and policy implications

OriginalPaper

Exploring contextual factors from consumer reviews affecting movie sales: an opinion mining approach

OriginalPaper

How can social commerce be boosted? The impact of consumer behaviors on the information dissemination mechanism in a social commerce network

OriginalPaper

The value of backers’ word-of-mouth in crowdfunding projects filtering: an empirical investigation

OriginalPaper

Allocating resources for a restaurant that serves regular and group-buying customers

OriginalPaper

Content shared between banks and users on the social ecosystem: an inductive exploratory inquiry