You may also want to move more towards content based recommendations. Essentially what that means is that you recommend characteristics of items and then do a search with the recommended characteristics as a query to find the recommended items.
As a bonus, you can also learn the degree of association between characteristics and items which helps the system downgrade spammers. On Mon, Nov 21, 2011 at 4:57 PM, Sebastian Schelter <[email protected]> wrote: > > > It would be impractical for the recommender > > to predict a rating on every single items before ranking them. > > In the standard item-based approach only items that are similar to the > ones that the user has interacted with need to be taken into account in > the recommendation phase. So you don't have to look at all 10 million > items using this approach. > > --sebastian >
