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 language||English (US)|
|Title of host publication||CIKM 2017 - Proceedings of the 2017 ACM Conference on Information and Knowledge Management|
|Publisher||Association for Computing Machinery|
|Number of pages||10|
|State||Published - Nov 6 2017|
|Event||26th ACM International Conference on Information and Knowledge Management, CIKM 2017 - Singapore, Singapore|
Duration: Nov 6 2017 → Nov 10 2017
|Name||International Conference on Information and Knowledge Management, Proceedings|
|Other||26th ACM International Conference on Information and Knowledge Management, CIKM 2017|
|Period||11/6/17 → 11/10/17|
Bibliographical noteFunding 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.
Acknowledgments. Œe work was supported in part by NSF grants IIS-1447566, IIS-1422557, CCF-1451986, CNS-1314560, IIS-0953274, IIS-1029711,
© 2017 ACM.