Tagtekin, BurakSahin, ZeynepÇakar, TunaDrias, Yassine2024-09-082024-09-082024979835038897897983503889612165-0608https://hdl.handle.net/20.500.11779/2335https://doi.org/10.1109/SIU61531.2024.10601038The present study has aimed to provide a different ranking approach that will be used actively in a sector-specific application regarding the optimization of item ranking presented to the users. The current online approach in several different applications still holds a manual ranking algorithm whose parameters are determined by the data specialists with adequate domain-knowledge. The obtained findings from the present study indicate that the optimized Bayesian Personalized Ranking models will be used for providing a suitable, data-driven input for the ranking system that would serve to be personalized. The outcomes of the present study also demonstrate that the model using LearnBPR optimized with a stochastic gradient descent algorithm outperform the other similar methods. The sample model outputs were also investigated by a user sample to ensure that the algorithm was working correctly. The next potential step is to provide a normalization process to include the extracted information to the current ranking system and observe the performance of this new algorithm with the A/B tests conducted.eninfo:eu-repo/semantics/closedAccessSmart SortingLearning To Rank (Ltr)Bayesian Personalized Ranking (Bpr)Cuisine RecommendationStochastic Gradient Descent OptimizationConference Object10.1109/SIU61531.2024.106010382-s2.0-85200922903