The article discusses the experimental setup for validating the effectiveness of DML through extensive offline experiments and online A/B testing. The evaluation utilizes two public datasets, MovieLens-1M and Amazon Electronics, focusing on tasks such as positive rating prediction and rating estimation. It outlines the data preparation process, ensuring that both rated and unrated items are balanced for user evaluations. The evaluation metrics AUC for classification tasks and Mean Squared Error for regression tasks are emphasized for measuring DML's performance, underscoring the complexity of relationships in the tasks used for analysis.
In this study, we conduct offline and online A/B testing to demonstrate the effectiveness of our proposed DML through varied datasets and careful evaluations.
Using MovieLens-1M and Amazon datasets, we introduce binary classification tasks and rigorously evaluate DML's performance based on precise metrics like AUC and MSE.
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