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
The Journal of Supercomputing
Erschienen in: The Journal of Supercomputing 1/2018

21.07.2017

Recommender system architecture based on Mahout and a main memory database

verfasst von: Zhegao Piao, Seong Joon Yoo, Yeong Hyeon Gu, Jaechun No, Zhiyan Jiang, Helin Yin

Erschienen in: The Journal of Supercomputing | Ausgabe 1/2018

Einloggen

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

search-config
loading …

Abstract

In this study, we propose a news recommendation system architecture using a main memory database (DB) and Mahout. The user’s news preference rate is calculated automatically based on the time the user spends reading news items and their length. While existing systems also infer the user’s preferred fields, our system adjusts the volume and ratio of news stories using these categories. We collect web pages accessed by the user on a smart device and classify them using a naive Bayes classifier to determine the user’s preferred news categories. Collaborative filtering is then used to search for related news items read by others and to recommend news in a ratio consistent with the user’s preferred fields. Using a main memory DB, recommendations are computed 2.1 times faster than with a traditional DB when recommending from among 100,000 items; further, the more data used for recommendations, the bigger the speed difference between the proposed and traditional systems becomes.
Vorheriger Artikel General queuing model for optimal seamless delivery of payload processing in multi-core processors
Nächster Artikel Resource-aware virtual machine placement algorithm for IaaS cloud

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

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

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
Literatur
1.
Dong H, Zhu J, Tang Y, Xu C, Ding R, Chen L (2015) UBS: a novel news recommendation system based on user behavior sequence. In: International Conference on Knowledge Science, Engineering and Management. Springer, Berlin, pp 738–750
2.
Kim SD, Park MG (2009) An adaptation system based on personalized web content items for mobile devices. KSII Trans Internet Inf Syst 3(6):628–646
3.
Liu J, Dolan P, Pedersen ER (2010) Personalized news recommendation based on click behavior. In: Proceedings of the 15th International Conference on Intelligent User Interfaces. ACM, New York, pp 31–40
4.
5.
6.
7.
8.
9.
10.
Sridevi M, Rajeshwara Rao R, Varaprasad Rao M (2016) A survey on recommender system. Int J Comput Sci Inf Secur 14(5):265–272
11.
Garcin F, Zhou K, Faltings B, Schickel V (2012) Personalized news recommendation based on collaborative filtering. In: Proceedings of the 2012 IEEE/WIC/ACM International Joint Conferences on Web Intelligence and Intelligent Agent Technology. IEEE Computer Society. 01, pp 437–441
12.
Zheng X, Lou Y, Xu Z, Yu Q, Lu L (2016) Tourism destination recommender system for the cold start problem. KSII Trans Internet Inf Syst 10(7):3192–3212
13.
Lika B, Kolomvatsos K, Hadjiefthymiades S (2014) Facing the cold start problem in recommender systems. Expert Syst Appl 41:2065–2073CrossRef
14.
Tavakolifard M, Gulla JA, Almeroth KC, Ingvaldesn JE, Nygreen G, Berg E (2013) Tailored news in the palm of your hand: a multi-perspective transparent approach to news recommendation. In: Proceedings of the 22nd International Conference on World Wide Web. ACM, New York, pp 305–308
15.
Bobadilla J, Ortega F, Hernando A, Gutiérrez A (2013) Recommender systems survey. Knowl Based Syst 46(2013):109–132CrossRef
16.
Pazzani MJ, Billsus D (2007) Content-based recommendation systems. In: Brusilovsky P, Kobsa A, Nejdl W (eds) The Adaptive Web. Springer, Berlin, pp 325–341
17.
Lops P, De Gemmis M, Semeraro G (2011) Content-based recommender systems: State of the art and trends. In: Ricci F, et al. (eds) Recommender Systems Handbook, pp 73–105
18.
Cantador I, Bellogín A, Vallet D (2010) Content-based recommendation in social tagging systems. In: Proceedings of the Fourth ACM Conference on Recommender Systems. pp 237–240
19.
Aizawa K, Tancharoen D, Kawasaki S, Yamasaki T (2004) Efficient retrieval of life log based on context and content. In: Proceedings of the 1st ACM Workshop on Continuous Archival and Retrieval of Personal Experiences. ACM, New York, pp 22–31
20.
Zhu H, Chen E, Xiong H, Yu K, Cao H, Tian J (2015) Mining mobile user preferences for personalized context-aware recommendation. ACM Trans Intell Syst Technol (TIST) 5(4):58
21.
Burke R (2007) Hybrid web recommender systems. In: Brusilovsky P, Kobsa A, Nejdl W (eds) The Adaptive Web. Springer, Berlin, pp 377–408
22.
Claypool M, Le P, Wased M, Brown D (2001) Implicit interest indicators. In: Proceedings of the 6th International Conference on Intelligent User Interfaces. ACM, New York, pp 33–40
23.
Kawai Y, Kumamoto T, Tanaka K (2007) Fair news reader: recommending news articles with different sentiments based on user preference. In: Bruno Apolloni B, Howlett RJ, Jain L (eds) Knowledge-Based Intelligent Information and Engineering Systems. Springer, Berlin, pp 612–622
24.
Lee HJ, Park SJ (2007) MONERS: a news recommender for the mobile web. Expert Syst Appl 32(1):143–150CrossRef
25.
Brunato M, Battiti R (2003) PILGRIM: A location broker and mobility-aware recommendation system. In: Proceedings of IEEE PerCom. pp 265–272
26.
Yeung KF, Yang Y (2010) A proactive personalized mobile news recommendation system. In: Developments in E-Systems Engineering (DESE). pp 207–212. doi:10.​1109/​DeSE.​2010.​40
27.
Thelwall M (2001) A web crawler design for data mining. J Inf Sci 27(5):319–325CrossRef
28.
Gu YH, Yoo SJ, Piao Z, Lin Y, Yan J, Park JH (2015) User preference analysis and visualization through the browser history of smart devices. In: Proceedings of the 2015 International Conference on Big Data Applications and Services. ACM, New York, pp 264–267
29.
Zhang W, Yoshida T, Tang X (2011) A comparative study of TF* IDF, LSI and multi-words for text classification. Expert Syst Appl 38(3):2758–2765CrossRef
30.
Zhang ML, Pena JM, Robles V (2009) Feature selection for multi-label naive Bayes classification. Inf Sci 179(19):3218–3229CrossRefMATH
31.
Chang HY, Huang SC, Lai CC (2014) A personalized IPTV channel-recommendation mechanism based on the MapReduce framework. J Supercomput 69(1):225–247CrossRef
32.
Li C, Lin M, Yang LT, Ding C (2014) Integrating the enriched feature with machine learning algorithms for human movement and fall detection. J Supercomput 67(3):854–865CrossRef
33.
Li CH, Gondra I, Liu L (2012) An efficient parallel neural network-based multi-instance learning algorithm. J Supercomput 62(2):724–740CrossRef
Metadaten
Titel
Recommender system architecture based on Mahout and a main memory database
verfasst von
Zhegao Piao
Seong Joon Yoo
Yeong Hyeon Gu
Jaechun No
Zhiyan Jiang
Helin Yin
Publikationsdatum
21.07.2017
Verlag
Springer US
Erschienen in
The Journal of Supercomputing / Ausgabe 1/2018
Print ISSN: 0920-8542
Elektronische ISSN: 1573-0484
DOI
https://doi.org/10.1007/s11227-017-2111-x

Weitere Artikel der Ausgabe 1/2018

The Journal of Supercomputing 1/2018 Zur Ausgabe

OriginalPaper

General queuing model for optimal seamless delivery of payload processing in multi-core processors

OriginalPaper

A novel buffer management scheme based on particle swarm optimization for SSD

OriginalPaper

On energy consumption of switch-centric data center networks

OriginalPaper

Interactive 3D simulation for fluid–structure interactions using dual coupled GPUs

OriginalPaper

DoS, impersonation and de-synchronization attacks against an ultra-lightweight RFID mutual authentication protocol for IoT

OriginalPaper

A high-performance and energy-efficient exhaustive key search approach via GPU on DES-like cryptosystems