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Total dataset is split into a training and test set in a 4:1 ratio.
Two measure metrics RMSE and MAE are used to evaluate the performance of the recommender algorithm.
RMSE emphasizes on larger absolute errors. The lower the RMSE is, the more accurate is the recommendation.
In the RMSE calculation formula, 𝑝_(𝑢,𝑖) is the predicted rating of user 𝑢 on item 𝑖, 𝑟_(𝑢,𝑖) is the actual rating of user 𝑢 on item 𝑖.
MAE is the most popular and commonly used measurement metrics to evaluate prediction model. It is a measure of deviation of recommendation from user¡¯s specific value.
In the MAS calculation formula, 𝑝_(𝑢,𝑖) the predicted rating from user u on item i , 𝑟_(𝑢,𝑖) is the actual rating from user u on item i, N is the total number of ratings on the item set. The lower the MAE, the better the recommendation algorithm predicts user¡¯s ratings.
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IEEE/ICACT20230130 Slide.18
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