Özet:

Abstract Recommendation systems have become increasingly popular in recent years due to the rise of large-scale online platforms that generate significant amounts of user data. However, traditional collaborative filtering methods like matrix decomposition have limitations when it comes to learning from user preferences, especially in situations where data sparsity and cold start problems exist. To address this, explicit feedback-based recommendation systems have gained attention for their ability to overcome these limitations. Explicit feedback-based systems use user feedback data such as ratings, clicks, and purchases to make personalized recommendations. A proposed solution to improve the efficiency of collaborative filtering is to combine the Deep Auto-Encoder Neural Network (DeepAEC) and One-Dimensional Traditional Neural Network (1D-CNN) approaches in a multi-task learning framework. This approach aims to address the limitations of traditional collaborative filtering methods by leveraging the strengths of both DeepAEC and 1D-CNN. Specifically, DeepAEC can be used to capture high-level representations of user preferences, while 1D-CNN can be used to learn more specific, local patterns in the user-item interaction data. The multi-task learning framework allows these two approaches to be combined to improve the accuracy and efficiency of the recommendation system.

Anahtar Kelimeler: