Low-Cost Experimentation with Recommendation Cohorts Using Member Attribute Segmentation
DOI:
https://doi.org/10.22399/ijcesen.5549Keywords:
Recommendation Systems, Attribute Segmentation, Cohort Experimentation, PersonalizationAbstract
This paper provides an in-depth overview of low-cost experimentation techniques in recommendation systems that rely on attribute-based segmentation of users. Cohort-based experimentation offers tailored testing and optimization by clustering users based on shared characteristics; hence, it reduces computational cost and improves scalability. Clustering algorithms such as K-Means can be used to evaluate algorithms and can be applied by systems to create coherent user cohorts.
The review also examines multiple areas, including model robustness, diffusion modeling, reasoning frameworks, and the challenge of data scarcity. Greater focus is given to socio-demographic factors, model performance across domains, and the ethical implications of algorithmic bias. Drawing on recent scholarly research publications, this paper underscores the importance of attribute segmentation in improving recommendation performance, enhancing user engagement, and promoting fairness. The results indicate that segmented cohorts play a significant role in optimizing system behavior, as well as supporting inclusion and responsible personalization strategies.
References
[1] Thiriveedhi, V. K., Krishnaswamy, D., Clunie, D., Pieper, S., Kikinis, R., & Fedorov, A. (2024). Cloud-based large-scale curation of medical imaging data using AI segmentation. Research Square, rs-3.
[2] Khandelwal, Y., Arvind, M., Kumar, S., Gupta, A., Danisetty, S. K., Bagad, P., ... & Tapaswi, M. (2024). Nurturenet: a multi-task video-based approach for newborn anthropometry. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 332-342).
[3] Evnine, A., Ioannidis, S., Kalimeris, D., Kalyanaraman, S., Li, W., Nir, I., ... & Weinsberg, U. (2024, August). Achieving a better tradeoff in multi-stage recommender systems through personalization. In Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (pp. 4939-4950).
[4] Falcinelli, C., Cheong, V. S., Ellingsen, L. M., & Helgason, B. (2024). Segmentation methods for quantifying X-ray Computed Tomography based biomarkers to assess hip fracture risk: a systematic literature review. Frontiers in Bioengineering and Biotechnology, 12, 1446829.
[5] Nind, J., Marra, C. A., Scahill, S., Mather, D., & Smith, A. (2024). The effects of free prescriptions on community pharmacy selection: A discrete choice experiment. Research in social and administrative pharmacy, 20(12), 1089-1095.
[6] Grijalva, N. (2024). Development, Implementation, and Validation of a Low-Cost Monocular Visual Localization System for Applications in Unmanned Aerial System Education (Master's thesis, New Mexico State University).
[7] Patkar, S., Chen, A., Basnet, A., Bixby, A., Rajendran, R., Chernet, R., ... & Jamaspishvili, T. (2024). Predicting the tumor microenvironment composition and immunotherapy response in non-small cell lung cancer from digital histopathology images. NPJ Precision Oncology, 8(1), 280.
[8] Di Martino, F., & Orciuoli, F. (2024). A computational framework to support the treatment of bedsores during COVID-19 diffusion. Journal of Ambient Intelligence and Humanized Computing, 15(1), 219-229.
[9] Mohr, Z., Olivares, A., & Piatak, J. (2024). Are public spaces welcoming to all? A conjoint experiment on cultural representation and inclusionary practices in museums. Public Administration, 102(3), 841-859.
[10] Seyfi, S., & Hall, C. M. (2024). Is gen Z a pro-SDG generation? A critical review and reflection. The Elgar companion to tourism and the sustainable development goals, 246-258.
[11] Fellini, I. (2024). Employment effects: replacement, substitution, complementarity and segmentation. In Research handbook on migration and employment (pp. 42-61). Edward Elgar Publishing.
[12] Jonnalagedda, P., Surprenant, R. L., Droser, M. L., & Bhanu, B. (2024). Searching for Life: end-to-end Automated Detection and Characterization of Ediacaran Biosignatures. IEEE Transactions on Geoscience and Remote Sensing, 62, 1-18.
[13] Shenoy, S., Iyer, A., & Zahedi, S. (2024). Phonics-based instruction and improvement in foundational reading skills of kindergartners in the Indian schooling context. Early Childhood Education Journal, 52(1), 73-85.
[14] Archetti, D., Venkatraghavan, V., Weiss, B., Bourgeat, P., Auer, T., Vidnyánszky, Z., ... & E-DADS (Early Detection of Alzheimer's Disease Subtypes) Consortium. (2024). A machine learning model to harmonize volumetric brain MRI data for quantitative neuroradiologic assessment of Alzheimer disease. Radiology: Artificial Intelligence, 7(1), e240030.
[15] Dawson, H., Davis, G., Ross, K., Miller, M. V., & Tomlinson, A. (2024). Using staged teaching and assessment approaches to facilitate inter-university collaboration and problem-based learning. Frontiers in public health, 12, 1334729.
[16] Goossens, Q., Locsin, M., Ponder, L. A., Chan, M., Ozmen, G. C., Prahalad, S., & Inan, O. T. (2024). Active vibrational Achilles tendon sensing for identifying and characterizing inflammatory symptomatology in enthesitis related arthritis. IEEE Transactions on Biomedical Engineering, 72(2), 645-654.
[17] Mölders, M., Bock, L., Barrantes, E., & Zülch, H. (2024). Exploring the emergence of finfluencers on Instagram: Survey-based descriptive insights into their segmentation, motivations, business models, and potential use in financial communication. HHL Leipzig Graduate School of Management.
[18] Watson, E., Viana, T., Zhang, S., Sturgeon, B., & Petersson, L. (2024). Towards an end-to-end personal fine-tuning framework for ai value alignment. Electronics, 13(20), 4044.
[19] Etkin, A., & Mathalon, D. H. (2024). Bringing imaging biomarkers into clinical reality in psychiatry. JAMA psychiatry, 81(11), 1142-1147.
[20] Khajehpiri, B., Granger, E., De Zambotti, M., Baker, F. C., Yuksel, D., & Forouzanfar, M. (2024). SleepBP-Net: A time-distributed convolutional network for nocturnal blood pressure estimation from photoplethysmogram. IEEE sensors journal, 24(12), 19590-19600.
[21] Gupta, K., Sinhal, R., & Badhiye, S. S. (2024). Remote photoplethysmography‐based human vital sign prediction using cyclical algorithm. Journal of Biophotonics, 17(1), e202300286.
[22] Prior, O., Macarro, C., Navarro, V., Monreal, C., Ligero, M., Garcia-Ruiz, A., ... & Perez-Lopez, R. (2024). Identification of precise 3D CT radiomics for habitat computation by machine learning in cancer. Radiology: Artificial Intelligence, 6(2), e230118.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2024 International Journal of Computational and Experimental Science and Engineering
This work is licensed under a Creative Commons Attribution 4.0 International License.
