Recommendation with capacity constraints

Konstantina Christakopoulou, Jaya Kawale, Arindam Banerjee

Research output: Chapter in Book/Report/Conference proceedingConference contribution

10 Scopus citations


In many recommendation settings, the candidate items for recommendation are associated with a maximum capacity, i e., number of seats in a Point-of-Interest (POI) or number of item copies in the inventory. However, despite the prevalence of the capacity constraint in the recommendation process, the existing recommendation methods are not designed to optimize for respecting such a constraint. Towards closing this gap, we propose Recommendation with Capacity Constraints - a framework that optimizes for both recommendation accuracy and expected item usage that respects the capacity constraints. We show how to apply our method to three state-of-the-art latent factor recommendation models: probabilistic matrix factorization (PMF), bayesian personalized ranking (BPR) for item recommendation, and geographical matrix factorization (GeoMF) for POI recommendation. Our experiments indicate that our framework is effective for providing good recommendations while taking the limited resources into consideration. Interestingly, our methods are shown in some cases to further improve the top-N recommendation quality of the respective unconstrained models.

Original languageEnglish (US)
Title of host publicationCIKM 2017 - Proceedings of the 2017 ACM Conference on Information and Knowledge Management
PublisherAssociation for Computing Machinery
Number of pages10
ISBN (Electronic)9781450349185
StatePublished - Nov 6 2017
Event26th ACM International Conference on Information and Knowledge Management, CIKM 2017 - Singapore, Singapore
Duration: Nov 6 2017Nov 10 2017

Publication series

NameInternational Conference on Information and Knowledge Management, Proceedings
VolumePart F131841


Other26th ACM International Conference on Information and Knowledge Management, CIKM 2017

Bibliographical note

Funding Information:
NASA grant NNX12AQ39A, and a gi‰ from Adobe Research. We also acknowledge technical support from the University of Minnesota Supercomputing Institute. KC would like to thank Evangelia Christakopoulou for the valuable comments.

Funding Information:
Acknowledgments. Œe work was supported in part by NSF grants IIS-1447566, IIS-1422557, CCF-1451986, CNS-1314560, IIS-0953274, IIS-1029711,

Publisher Copyright:
© 2017 ACM.


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