Cold-start user representation learning method based on cross-domain meta-learning framework
To solve the two problems of over-reliance on overlapping users and poor generalization ability due to data sparsity in cold-start scenarios,which existed in cross-domain recommendation methods,and took advantage of meta-learning's ability to quickly adapt to data-sparse tasks,a cold-start user representation learning method based on a cross-domain meta-learning framework was proposed.A multi-level attention fusion mechanism was first designed,where the gate recurrent unit extracted the user's short-term preferences and the multi-level feature attention fused the user's long short-term preferences in the source domain to obtain the user's generalized representation.A meta-network was designed to train the initialization parameters of the mapping function to transfer the user's preferences in the source domain to the target domain to obtain the initial embedded representation of the cold-start user in the target domain and used it to make a recommendation to achieve better results.Three cross-domain recommendation tasks were constructed using the Amazon dataset,and extensive experiments were conducted,the test results indicated that the model in this study outperformed other baseline models in terms of both mean absolute error and root mean square error evaluations.
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