Evaluation and Deployment
Model Evaluation
First and foremost, you should assess how well your model is performing. If you recall, we used metrics like RMSE (Root Mean Square Error) and MAE (Mean Absolute Error) during cross-validation. These metrics can give you a quantitative idea of how well your model is doing. A smaller RMSE or MAE value typically means better recommendations, but that's not the only metric you should consider.
In addition to these metrics, it is important to consider other factors that can indicate the success of your recommender system. For instance, you could also look into business-related Key Performance Indicators (KPIs) to evaluate the impact of your system. These KPIs might include increased sales, higher customer engagement, or even a boost in customer reviews and ratings after implementing your recommender system. By examining these additional metrics, you can gain a more comprehensive understanding of the effectiveness and value of your model in...