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https://hdl.handle.net/20.500.11779/2335
Title: | The Application of Two Bayesian Personalized Ranking Approaches based on Item Recommendation from Implicit Feedback; | Other Titles: | Örtük Geri Bildirime Dayalı Öğe Tavsiyesi İçin İki Bayes Kişiselleştirilmiş Sıralama Yaklaşımının Uygulanması | Authors: | Tağtekin,B. Şahin,Z. Çakar,T. Drias,Y. |
Keywords: | Bayesian personalized ranking (BPR) cuisine recommendation learning to rank (LTR) smart sorting stochastic gradient descent optimization |
Publisher: | Institute of Electrical and Electronics Engineers Inc. | Abstract: | The 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. © 2024 IEEE. | Description: | Berdan Civata B.C.; et al.; Figes; Koluman; Loodos; Tarsus University | URI: | https://doi.org/10.1109/SIU61531.2024.10601038 https://hdl.handle.net/20.500.11779/2335 |
ISBN: | 979-835038896-1 |
Appears in Collections: | Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection |
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