Hi, I want to know how to use the decomposition of the rating matrix to make recommendations.
If I want to predict a user preference for an item, I simply calculate the dot product of the user's row in the user-features matrix and the item's column in the features-items matrix. But what if I want to recommend N items to a user? Should I predict his preference for all items the same way, and just return the top N? Will it still be scalable? Or maybe there is another way to do this? I've read some papers on SVD explaining that it is also possible to use the small matrices to obtain a user/ an item neighborhood based on less data. Is it implemented in Mahout? Which way is better? I'd be grateful for some help. Thanks, Maya --------------------------------------------------------------------- Intel Electronics Ltd. This e-mail and any attachments may contain confidential material for the sole use of the intended recipient(s). Any review or distribution by others is strictly prohibited. If you are not the intended recipient, please contact the sender and delete all copies.
