Improving Cross-Domain Recommendation Methods with Factorization Machine Integration C apraz Alan O neri Yo ntemlerinin Fakto rizasyon Makinesi Entegrasyonu ile Iyiles tirilmesi

Date

2025-01-01

Author

Colak, Ahmet Eren
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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Cross-domain recommender systems aim to im- prove recommendations in the target domain by exploiting the source domain where abundant data is present. In this study, we investigate the combination of a shallow model, the factorization machine, with cross-domain models to improve click-through rate prediction, which has a significant impact on online advertising systems. Deep neural network based models perform well in predicting click-through rates and are strong at capturing non- linear interactions. However, while deep models capture complex correlations, they may miss superficial relationships between users and items. In this study, the integration of a shallow model, the factorization machine, with deep cross-domain models is presented as an important approach to capture low-order interactions and obtain more meaningful embedding vectors. Experiments show that the combination of FM and cross-domain models leads to a significant performance improvement in click-through rate prediction.

Subject Keywords

click-through rate prediction, cross-domain recommendation, factorization machine

URI

https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=105015541834&origin=inward
https://hdl.handle.net/11511/115932

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Department of Computer Engineering, Conference / Seminar