From my research, a recommendation system are a subclass of information filtering system that seek to predict the rating or preference that a user would give to an item. And basically exists many type of recommendation systems, such as collaborative filtering and content-based. One important aspect, is that evaluation is important in assessing the effectiveness of recommendation algorithms. The commonly used metrics are the mean squared error and root mean squared error, and also there exists others such as precision an recall.My question is after I developed a recommendation system, and after evaluating using those metrics, how can I consider that my system provides good quality recommendations, or in others words, what should be the tresholds or MSE or RMSE values to be considered a good recommendation system.Thanks. Any suggestions are welcomed.
How can conclusions be drawn from recommendation systems evaluation?
predictive modeling;recommender system;information retrieval;evaluation