Please use this identifier to cite or link to this item:
https://hdl.handle.net/20.500.11779/2335
Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Tağtekin,B. | - |
dc.contributor.author | Şahin,Z. | - |
dc.contributor.author | Çakar,T. | - |
dc.contributor.author | Drias,Y. | - |
dc.date.accessioned | 2024-09-08T16:52:57Z | - |
dc.date.available | 2024-09-08T16:52:57Z | - |
dc.date.issued | 2024 | - |
dc.identifier.isbn | 979-835038896-1 | - |
dc.identifier.uri | https://doi.org/10.1109/SIU61531.2024.10601038 | - |
dc.identifier.uri | https://hdl.handle.net/20.500.11779/2335 | - |
dc.description | Berdan Civata B.C.; et al.; Figes; Koluman; Loodos; Tarsus University | en_US |
dc.description.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. | en_US |
dc.language.iso | en | en_US |
dc.publisher | Institute of Electrical and Electronics Engineers Inc. | en_US |
dc.relation.ispartof | 32nd IEEE Conference on Signal Processing and Communications Applications, SIU 2024 - Proceedings -- 32nd IEEE Conference on Signal Processing and Communications Applications, SIU 2024 -- 15 May 2024 through 18 May 2024 -- Mersin -- 201235 | en_US |
dc.rights | info:eu-repo/semantics/closedAccess | en_US |
dc.subject | Bayesian personalized ranking (BPR) | en_US |
dc.subject | cuisine recommendation | en_US |
dc.subject | learning to rank (LTR) | en_US |
dc.subject | smart sorting | en_US |
dc.subject | stochastic gradient descent optimization | en_US |
dc.title | The Application of Two Bayesian Personalized Ranking Approaches based on Item Recommendation from Implicit Feedback; | en_US |
dc.title.alternative | Örtük Geri Bildirime Dayalı Öğe Tavsiyesi İçin İki Bayes Kişiselleştirilmiş Sıralama Yaklaşımının Uygulanması | en_US |
dc.type | Conference Object | en_US |
dc.identifier.doi | 10.1109/SIU61531.2024.10601038 | - |
dc.identifier.scopus | 2-s2.0-85200922903 | en_US |
dc.authorscopusid | 57224638412 | - |
dc.authorscopusid | 58876518800 | - |
dc.authorscopusid | 56329345400 | - |
dc.authorscopusid | 56440023300 | - |
dc.identifier.wosquality | N/A | - |
dc.identifier.scopusquality | N/A | - |
dc.relation.publicationcategory | Konferans Öğesi - Uluslararası - Kurum Öğretim Elemanı | en_US |
dc.department | Mef University | en_US |
item.openairecristype | http://purl.org/coar/resource_type/c_18cf | - |
item.grantfulltext | none | - |
item.languageiso639-1 | en | - |
item.cerifentitytype | Publications | - |
item.fulltext | No Fulltext | - |
item.openairetype | Conference Object | - |
Appears in Collections: | Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection |
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