Skip to main content
SpringerLink
Log in

A Product Feature-Based User-Centric Ranking Model for E-Commerce Search

  • Conference paper
  • First Online:
Experimental IR Meets Multilinguality, Multimodality, and Interaction (CLEF 2016)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9822))

Abstract

During the online shopping process, users search for interesting products in order to quickly access those that fit with their needs among a long tail of similar or closely related products. Our contribution addresses head queries that are frequently submitted on e-commerce Web sites. Head queries usually target featured products with several variations, accessories, and complementary products. We present in this paper a product feature-based user-centric model for product search involving, in addition to product characteristics, the user engagement toward the product. This model has been evaluated through the product search track of the LL4IR lab at CLEF 2015 in order to highlight the effectiveness of our model as well as the impact of the user engagement factor.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    http://www.ecommercebytes.com/cab/abn/y14/m07/i15/s04.

  2. 2.

    http://www.businesswire.com/news/home/20150105005186/en/Amazon-Sellers-Sold-Record-Setting-2-Billion-Items.

  3. 3.

    http://www.godatafeed.com/resources/google-shopping-campaigns.

  4. 4.

    http://schema.org.

  5. 5.

    http://facebook.com.

  6. 6.

    http://www.regiojatek.hu/.

  7. 7.

    http://blog.lemonstand.com/7-ways-optimize-product-page-conversions/.

References

  1. Balog, K., Kelly, L., Schuth, A.: Head first: living labs for ad-hoc search evaluation. In: CIKM 2014, pp. 1815–1818. ACM (2014)

    Google Scholar 

  2. Castagnos, S., Jones, N., Pu, P.: Eye-tracking product recommenders’ usage. In: RecSys 2010. ACM (2010)

    Google Scholar 

  3. Chen, L.: Social influence of product popularity on consumer decisions:usability study of flickr camera finder. In: IUI 2010, pp. 297–300 (2010)

    Google Scholar 

  4. Chen, X., Wang, H., Sun, X., Pan, J., Yu, Y.: Diversifying product search results. In: SIGIR 2011, pp. 1093–1094. ACM (2011)

    Google Scholar 

  5. Corbitt, B.J., Thanasankit, T., Yi, H.: Trust and e-commerce: a study of consumer perceptions. Electron. Commer. Res. Appl. 2(3), 203–215 (2003)

    Article  Google Scholar 

  6. Craswell, N., Zaragoza, H., Robertson, S.: Microsoft cambridge at TREC 14: enterprise track. In: TREC 2005 (2005)

    Google Scholar 

  7. Dakka, W., Gravano, L., Ipeirotis, P.G.: Answering general time-sensitive queries. IEEE ToKDE 24, 220–235 (2012)

    Google Scholar 

  8. Detlor, B., Sproule, S., Gupta, C.: Pre-purchase online information seeking: search versus browse. J. Electron. Commer. Res. 4(2), 72–84 (2003)

    Google Scholar 

  9. Duan, H., Zhai, C.X., Cheng, J., Gattani, A.: Supporting keyword search in product database: a probabilistic approach. Proc. VLDB Endow. 6(14), 1786–1797 (2013)

    Article  Google Scholar 

  10. Ghirmatsion, A.B., Balog, K.: Probabilistic field mapping for product search. In: CLEF 2015 Workshop (2015)

    Google Scholar 

  11. Mothe, J., Savoy, J., Kamps, J., Pinel-Sauvagnat, K., Jones, G., San Juan, E., Capellato, L., Ferro, N. (eds.): CLEF 2015. LNCS, vol. 9283. Springer, Heidelberg (2015)

    Google Scholar 

  12. Joachims, T.: Optimizing search engines using clickthrough data. In: KDD 2002, pp. 133–142 (2002)

    Google Scholar 

  13. Lathia, N., Hailes, S., Capra, L., Amatriain, X.: Temporal diversity in recommender systems. In: SIGIR 2010, pp. 210–217. ACM (2010)

    Google Scholar 

  14. Lehmann, J., Lalmas, M., Yom-Tov, E., Dupret, G.: Models of user engagement. In: Masthoff, J., Mobasher, B., Desmarais, M.C., Nkambou, R. (eds.) UMAP 2012. LNCS, vol. 7379, pp. 164–175. Springer, Heidelberg (2012)

    Chapter  Google Scholar 

  15. Osiński, S., Weiss, D.: A concept-driven algorithm for clustering search results. IEEE Intell. Syst. 20(3), 48–54 (2005)

    Article  Google Scholar 

  16. Schaer, P., Tavakolpoursaleh, N.: Historical clicks for product search: Gesis at clef ll4ir 2015. In: CLEF 2015 Workshop (2015)

    Google Scholar 

  17. Schuth, A., Balog, K., Kelly, L.: Overview of the living labs for information retrieval evaluation (LL4IR) CLEF lab 2015. In: Mothe, J., Savoy, J., Kamps, J., Pinel-Sauvagnat, K., Jones, G., San Juan, E., Capellato, L., Ferro, N. (eds.) CLEF 2015. LNCS, vol. 9283, pp. 484–496. Springer, Heidelberg (2015). doi:10.1007/978-3-319-24027-5_47

    Chapter  Google Scholar 

  18. Vandic, D., van Dam, J.-W., Frasincar, F.: Faceted product search powered by the semantic web. Dec. Supp. Systems 53(3), 425–437 (2012)

    Article  Google Scholar 

  19. Xiang, L., Yuan, Q., Zhao, S., Chen, L., Zhang, X., Yang, Q., Sun, J.: Temporal recommendation on graphs via long- and short-term preferencefusion. In: KDD 2010, pp. 723–732. ACM(2010)

    Google Scholar 

  20. Zaragoza, H., Craswell, N., Taylor, M.J., Saria, S., Robertson, S.E.: Microsoft cambridge at TREC 13: web and hard tracks. In: TREC 2004 (2004)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. IRIT, Université de Toulouse, CNRS, UPS, 118 Route Narbonne, Toulouse, France

    Lamjed Ben Jabeur, Lynda Tamine & Paul Mousset

  2. Sorbonne Universités, UPMC Univ Paris 06, LIP6 UMR 7606, 75005, Paris, France

    Laure Soulier

Authors
  1. Lamjed Ben Jabeur
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Laure Soulier
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Lynda Tamine
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Paul Mousset
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Lamjed Ben Jabeur .

Editor information

Editors and Affiliations

  1. Universität Duisburg-Essen , Duisburg, Germany

    Norbert Fuhr

  2. Universidade de Évora , Évora, Portugal

    Paulo Quaresma

  3. University of Évora , Évora, Portugal

    Teresa Gonçalves

  4. Aalborg University Copenhagen , Copenhagen, Denmark

    Birger Larsen

  5. University of Stavanger , Stavanger, Norway

    Krisztian Balog

  6. University of Glasgow , Glasgow, United Kingdom

    Craig Macdonald

  7. University of Padua , Padua, Italy

    Linda Cappellato

  8. University of Padua , Padua, Italy

    Nicola Ferro

Rights and permissions

Reprints and permissions

Copyright information

© 2016 Springer International Publishing Switzerland

About this paper

Cite this paper

Jabeur, L.B., Soulier, L., Tamine, L., Mousset, P. (2016). A Product Feature-Based User-Centric Ranking Model for E-Commerce Search. In: Fuhr, N., et al. Experimental IR Meets Multilinguality, Multimodality, and Interaction. CLEF 2016. Lecture Notes in Computer Science(), vol 9822. Springer, Cham. https://doi.org/10.1007/978-3-319-44564-9_14

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-44564-9_14

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-44563-2

  • Online ISBN: 978-3-319-44564-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation