Skip to main content
Disciplines
Automotive Business IT + Informatics Construction + Real Estate Electrical Engineering + Electronics Energy + Sustainability Insurance + Risk Finance + Banking Management + Leadership Marketing + Sales Mechanical Engineering + Materials
Events
DE
EN
Books
Journals
Topic Page
Marketing
Start single access now
Access for companies
EXTENDED SEARCH
Log in
Top
Published in:
Real-Time Target Tracking Through Mobile Crowdsensing Read chapter Read first chapter

2017 | OriginalPaper | Chapter

Modeling Complementary Relationships of Cross-Category Products for Personal Ranking

Authors : Wenli Yu, Li Li, Fei Hu, Fan Li, Jinjing Zhang

Published in: Web Information Systems Engineering – WISE 2017

Publisher: Springer International Publishing

Log in

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

Abstract

The category of the product acts as the label of the product. It also exemplifies users various needs and tastes. In the existing recommender systems, the focus is on similar products recommendation with little or no intention to investigate the cross-category and the complementary relationship between categories and products. In this paper, a novel method based on Bayesian Personalized Ranking (BPR) is proposed to integrate the complementary information between categories and the latent features of both users and items for better recommendation. By considering category dimensions explicitly, the model can alleviate the cold start issue and give the recommendation not only considering traditional similarity measure but complementary relationships between products as well. The method is evaluated comprehensively and the experimental results illustrate that our work optimized ranking significantly (with high recommendation performance).

Dont have a licence yet? Then find out more about our products and how to get one now:

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!

inform now

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!

inform now

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!

inform now
previous chapter How Fair Is Your Network to New and Old Objects?: A Modeling of Object Selection in Web Based User-Object Networks
next chapter Eliminating Incorrect Cross-Language Links in Wikipedia
Literature
1.
Resnick, P., Varian, H.R.: Recommender systems. Commun. ACM 40(3), 56–58 (1997)CrossRef
2.
Koren, Y.: Collaborative filtering with temporal dynamics. Commun. ACM 53(4), 89–97 (2010)CrossRef
3.
Wang, J., De Vries, A.P.: Unifying user-based and item-based collaborative filtering approaches by similarity fusion. In: SIGIR, pp. 501–508. ACM (2006)
4.
Hofmann, T.: Latent semantic models for collaborative filtering. TOIS 22(1), 89–115 (2004)CrossRef
5.
Kalantidis, Y., Kennedy, L., Li, L.J.: Getting the look: clothing recognition and segmentation for automatic product suggestions in everyday photos. In: International Conference on Multimedia Retrieval, pp. 105–112 (2013)
6.
He, R., Fang, C., Wang, Z., Mcauley, J.: Vista: a visually, socially, and temporally-aware model for artistic recommendation. In: ACM Conference on Recommender Systems, pp. 309–316 (2016)
7.
He, R., Mcauley, J.: Ups and downs: modeling the visual evolution of fashion trends with one-class collaborative filtering. In: International Conference on World Wide Web, pp. 507–517 (2016)
8.
Ziegler, C.N., Lausen, G., Schmidt, L.: Taxonomy-driven computation of product recommendations. In: Proceedings of the Thirteenth ACM International Conference on Information and Knowledge Management, pp. 406–415. ACM (2004)
9.
Weng, L.-T., Xu, Y., Li, Y.: Exploiting item taxonomy for solving cold-start problem in recommendation making. In: ICTAI, vol. 2, pp. 113–120. IEEE (2008)
10.
Mnih, A.: Taxonomy-informed latent factor models for implicit feedback. In: KDD Cup, pp. 169–181 (2012)
11.
Zhang, Y., Ahmed, A., Josifovski, V., Smola, A.: Taxonomy discovery for personalized recommendation. In: WSDM, pp. 243–252. ACM (2014)
12.
Wang, S., Tang, J., Wang, Y., Liu, H.: Exploring implicit hierarchical structures for recommender systems. In: IJCAI, pp. 1813–1819 (2015)
13.
Koenigstein, N., Dror, G., Koren, Y.: Yahoo! music recommendations: modeling music ratings with temporal dynamics and item taxonomy. In: Recsys, pp. 165–172. ACM (2011)
14.
Rendle, S., Freudenthaler, C., Gantner, Z., Schmidt-Thieme, L.: BPR: Bayesian personalized ranking from implicit feedback. In: CUAI, pp. 452–461 (2009)
15.
Linden, G., Smith, B., York, J.: Amazon.com recommendations: item-to-item collaborative filtering. IEEE Internet Comput. 7(1), 76–80 (2003)CrossRef
Metadata
Title
Modeling Complementary Relationships of Cross-Category Products for Personal Ranking
Authors
Wenli Yu
Li Li
Fei Hu
Fan Li
Jinjing Zhang
Copyright Year
2017
Book
Web Information Systems Engineering – WISE 2017

Print ISBN: 978-3-319-68785-8

Electronic ISBN: 978-3-319-68786-5

Copyright Year: 2017

DOI
https://doi.org/10.1007/978-3-319-68786-5_8

Premium Partner

    Image Credits