A Survey of Statistical Models and Algorithms in Personalized Recommender Systems: The Case of Skincare Recommendation
DOI:
https://doi.org/10.54097/xz637r69Keywords:
Personalized Recommendation, Skincare Recommendation, Matrix Factorization, Deep Learning, Graph Neural Networks.Abstract
In the era of information overload, personalized recommender systems are essential, particularly in critical domains such as skincare where incorrect suggestions have large physical risks. This presents a specialized problem whose demands for accuracy, explainability, and safety in the algorithms are unprecedented. This paper presents an extensive technical roadmap to the researcher, systematically examining the developmental history of recommender algorithms, covering the principal collaborative filtering and content-based filtering algorithms, the latent factor revolution with matrix factorization algorithms, and finishing with the newest deep learning and graph neural network-based algorithms for these systems. Some of this work covers the newest areas of research into how present systems can be trained to become "trustworthy decision partners", discussing causal inference for debiasing techniques, explainability techniques for building trust in users, and multi-dimensional, performance measures beyond accuracy. Using skin-care as a core subject, this paper examines how far the above paradigms are applicable to a very complex and critical real-world scenario. This will also provide an interesting view of future developments in hyper-personalized recommendation.
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