Cold Start Recommendation based on Cross-domain Sharing Knowledge Transfer Learning

Due to the sparse initial interaction data, the cold-start problem poses a challenge to recommendation networks, e-commerce websites, and video platforms, etc. Traditional user profiling can predict preferences but raises privacy concerns. To address this issue, this paper proposes a cross-domain knowledge-shared transfer learning model that is initially trained on a cross-domain mixed dataset to allow multi-task learning. This shared knowledge enhances the generalizability of the model and minimizes the dependence on sparse data. Different from traditional recommender systems, this model can utilize limited user-item interaction data to make accurate recommendations and reduce the cold-start problem. After initial training, the model is fine-tuned on the target domain data. To avoid overfitting and inefficiency, an extended Transformer module is used, extracting multi-scale hidden states and a Transformer encoder, which are updated at each time step. Experimental results on the Amazon dataset show that the model can adapt to new tasks more efficiently, reducing computational cost and risk of overfitting.