Abstract
Recommendation systems are highly interested in technology companies nowadays. The businesses are constantly growing users and products, causing the number of users and items to continuously increase over time, to very large numbers, this leads to the cold-start problem. Recommending purely cold-start users is a long-standing and fundamental challenge in the recommendation systems where systems are unable to recommend relevant items to the users due to unavailability of adequate information about them. To solve this problem, extensive studies have been carried out using the side information techniques (user information, item information, ...). However, we argue that this work will affect the user/product group that had a lot of interaction, using this side information can reduce the performance of the model when just focusing on learning based on the side information. In this paper, we propose a combination of global and local side Information Fusion Techniques based on attention algorithm applied to Graph neural network-based models for cold-start users recommendation, and we call this architecture GIFT4Rec.
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Appendix B
Appendix B
We defines \(\Theta \) and \(\Theta _{WG}\) as our model parameters and the parameters of Weight Generated module. In addition, \(scores_{u_B}\) and \(scores_{u_{B_{info}}}\) denote users \(u_{B}\) (batch of U) model performance in validation set if just using their behavior embedding or their side information. Here is a pseudo code for our proposed model learning algorithm.
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Nguyen, TNL. et al. (2023). GIFT4Rec: An Effective Side Information Fusion Technique Apply to Graph Neural Network for Cold-Start Recommendation. In: Nguyen, N.T., et al. Intelligent Information and Database Systems. ACIIDS 2023. Lecture Notes in Computer Science(), vol 13995. Springer, Singapore. https://doi.org/10.1007/978-981-99-5834-4_27
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DOI: https://doi.org/10.1007/978-981-99-5834-4_27
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