Skip to main content

A Chain Composite Item Recommender for Lifelong Pathways

  • 445 Accesses

Part of the Lecture Notes in Computer Science book series (LNISA,volume 12925)


This work addresses the problem of recommending lifelong pathways, i.e., sequences of actions pertaining to health, social or professional aspects, for fulfilling a personal lifelong project. This problem raises some specific challenges, since the recommendation process is constrained by the user profile, the time they can devote to the actions in the pathway, the obligation to smooth the learning curve of the user. We model lifelong pathways as particular chain composite items and formalize the recommendation problem as a form of orienteering problem. We adapt classical evaluation criteria for measuring the quality of the recommended pathways. We experiment with both artificial and real datasets, showing our approach is a promising building block of an interactive lifelong pathways recommender system.


  • Chain composite item recommendation
  • Orienteering problem

Funded by ANRT CIFRE 2020/0731.

This is a preview of subscription content, access via your institution.

Buying options

USD   29.95
Price excludes VAT (USA)
  • DOI: 10.1007/978-3-030-86534-4_5
  • Chapter length: 12 pages
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
USD   54.99
Price excludes VAT (USA)
  • ISBN: 978-3-030-86534-4
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Softcover Book
USD   69.99
Price excludes VAT (USA)
Fig. 1.
Fig. 2.
Fig. 3.
Fig. 4.
Fig. 5.
Fig. 6.
Fig. 7.


  1. 1.

  2. 2.

    RSA stands for Revenue de Solidarité Active and is French form of in work welfare benefit aimed at reducing the barrier to return to work.

  3. 3.

    These are the most related official indicators that we found on the topic of social assistance giving hints how to set these thresholds in case of RSA social benefit.


  1. Aligon, J., Gallinucci, E., Golfarelli, M., Marcel, P., Rizzi, S.: A collaborative filtering approach for recommending OLAP sessions. DSS 69, 20–30 (2015)

    Google Scholar 

  2. Amer-Yahia, S., Roy, S.B.: Interactive exploration of composite items. In: EDBT, pp. 513–516 (2018)

    Google Scholar 

  3. Cao, X., Chen, L., Cong, G., Xiao, X.: Keyword-aware optimal route search. Proc. VLDB Endow. 5(11), 1136–1147 (2012)

    CrossRef  Google Scholar 

  4. Chanson, A., Labroche, N., Marcel, P., T’Kindt, V.: The traveling analyst problem, orienteering applied to exploratory data analysis. In: ROADEF (2021)

    Google Scholar 

  5. Drushku, K., Aligon, J., Labroche, N., Marcel, P., Peralta, V.: Interest-based recommendations for business intelligence users. Inf. Syst. 86, 79–93 (2019)

    CrossRef  Google Scholar 

  6. Chanson, A., et al.: The traveling analyst problem: definition and preliminary study. In: DOLAP, pp. 94–98 (2020)

    Google Scholar 

  7. Garey, M.R., Johnson, D.S.: “Strong” NP-completeness results: motivation, examples, and implications. J. ACM 25(3), 499–508 (1978)

    Google Scholar 

  8. Gionis, A., Lappas, T., Pelechrinis, K., Terzi, E.: Customized tour recommendations in urban areas. In: WSDM, pp. 313–322 (2014)

    Google Scholar 

  9. Guha, R.V., Gupta, V., Raghunathan, V., Srikant, R.: User modeling for a personal assistant. In: WSDM, pp. 275–284 (2015)

    Google Scholar 

  10. Roy, S.B., Das, G., Amer-Yahia, S., Yu, C.: Interactive itinerary planning. In: ICDE, pp. 15–26 (2011)

    Google Scholar 

  11. Son, L.H.: Dealing with the new user cold-start problem in recommender systems: a comparative review. Inf. Syst. 58, 87–104 (2016)

    CrossRef  Google Scholar 

  12. Tsiligirides, T.: Heuristic methods applied to orienteering. J. Oper. Res. Soc. 35(9), 797–809 (1984)

    CrossRef  Google Scholar 

  13. Vansteenwegen, P., Gunawan, A.: Orienteering Problems. EURO Advanced Tutorials on Operational Research (2019)

    Google Scholar 

  14. Wang, H., Song, Y., Chang, M., He, X., White, R.W., Chu, W.: Learning to extract cross-session search tasks. In: WWW, pp. 1353–1364 (2013)

    Google Scholar 

  15. Xing, E.P., Ng, A.Y., Jordan, M.I., Russell, S.J.: Distance metric learning with application to clustering with side-information. In: NIPS, pp. 505–512 (2002)

    Google Scholar 

Download references

Author information

Authors and Affiliations


Corresponding author

Correspondence to Patrick Marcel .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and Permissions

Copyright information

© 2021 Springer Nature Switzerland AG

About this paper

Verify currency and authenticity via CrossMark

Cite this paper

Chanson, A., Devogele, T., Labroche, N., Marcel, P., Ringuet, N., T’Kindt, V. (2021). A Chain Composite Item Recommender for Lifelong Pathways. In: Golfarelli, M., Wrembel, R., Kotsis, G., Tjoa, A.M., Khalil, I. (eds) Big Data Analytics and Knowledge Discovery. DaWaK 2021. Lecture Notes in Computer Science(), vol 12925. Springer, Cham.

Download citation

  • DOI:

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-86533-7

  • Online ISBN: 978-3-030-86534-4

  • eBook Packages: Computer ScienceComputer Science (R0)