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7 shows the plots at different x values. It can be observed from the plots that the MAE values get better as we increase the model size and the improvements are drastic at the beginning, but gradually slows down as we increase the model size. The most important observation from these plots is the high accuracy can be achieved using only a fraction of items. For example, at x=0.3 the full item-item scheme provided an MAE of 0.7873, but using a model size of only 25, we were able to achieve an MAE value of 0.842. At x=0.8 these numbers are even more appealing-for the full item-item we had an MAE of 0.726 but using a model size of only 25 we were able to obtain an MAE of 0.754, and using a model size of 50 the MAE was 0.738. In other words, at x=0.8 we were within and of the full item-item scheme's accuracy using only and of the items respectively!
This model size sensitivity has important performance implications. It appears from the plots that it is useful to precompute the item similarities using only a fraction of items and yet possible to obtain reasonably good prediction quality.
Next: Impact of the model Up: Experimental Evaluation Previous: Performance Results Badrul M. Sarwar