The purpose of this article is to study the main approaches and algorithms for building recommendation services in online trading systems.Recommendation systems optimize the sales process by identifying interesting and relevant products or services for users and offering them personalized recommendations, and serve to increase sales in e-commerce systems.In this article, the capabilities of the main techniques of recommendation systems, such as collaborative filtering, content-based approaches and hybrid models have been analyzed.A comparative analysis of the effectiveness of these methods and algorithms is carried out, their advantages and disadvantages are identified.The paper concludes by suggesting theoretical and practical frameworks for future advancements in recommender systems.
| Jurnal | ТАТУ хабарлари |
|---|---|
| Nashr sanasi | 2024-11-20 |
| DOI | 10.61663/243tuitmct6 |
DOI: 10.61663/243tuitmct6 · Maqolaning asl sahifasi
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