Leveraging In-Context Learning to Transfer Cross-Domain Knowledge in Click-Through Rate Prediction Baglam I i grenme Kullanilarak Tiklama Orani Tahminlerine apraz Alan Bilgilerinin Aktarilmasi

Date

2025-01-01

Author

Aydogdu, Mehmet Erdeniz
Sengor Altingovde, Ismail
Karagöz, Pınar
Toroslu, İsmail Hakkı

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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With the development of large language models, natural language tasks have seen significant improvements, including personalized recommendation. Traditional approaches in recommendation are often based on collaborative filtering, which relies on historical interactions of similar users. These methods struggle with cold-start and data sparsity issues. Cross-domain recommendation systems try to tackle these problems by leveraging knowledge from a richer domain to increase recommendation performance. However, this is a tedious task as it needs to correlate between two different domain knowledge. Pre-trained LLMs (Large Language Models), on the other hand, can tackle these problems thanks to their parametric knowledge and the ability to generate rich representations of user preferences and contextual information. This article analyzes the use of pre-trained LLMs relying on the parametric knowledge and in-context learning for Click-Through Rate (CTR) prediction.

Subject Keywords

cross-domain recommendation, CTR, large language models, recommendation

URI

https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=105015535207&origin=inward
https://hdl.handle.net/11511/115736

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Department of Computer Engineering, Conference / Seminar