Subject: "LLR with time"

Part of what Ted is talking about can be seen in the carousels on Netflix or Amazon. Some are not recommendations like “trending” videos, or “new” videos, or “prime” videos (substitute your own promotions here). Nothing to do with recommender created items but presented along with recommender-based carousels. They are based on analytics or business rules and ideally have some randomness built in. The reason for this is 1) it works by exposing users to items that they would not see in recommendations and 2) it provides data to build the recommender model from.

A recommender cannot work in an app that has no non-recommended items displayed or there will be no un-biased data to create recommendations from. This would lead to crippling overfitting. Most apps have placements like the ones mentioned above and also have search and browse. However you do it, it must be prominent and aways available. The moral of this paragraph is; don’t try to make everything a recommendation, it will be self-defeating. In fact make sure not every video watch comes from a recommendation.

Likewise think of placements (reflecting a particular recommender use) as experimentation grounds. Try things like finding a recommended category and then recommending items in that category all based on user behavior. Or try a placement based on a single thing a user watched like “because you watched xyz you might like these”. Don’t just show the most popular categories for the user and recommend items in them. This would be a type of overfitting too.

I’m sure we have strayed far from your original question but maybe it’s covered somewhere in here.
On Nov 12, 2017, at 12:11 PM, Johannes Schulte <[EMAIL PROTECTED]> wrote:

I did "second order" recommendations before but more to fight sparsity and
find more significant associations in situations with less traffic, so
recommending categories instead of products. There needs to be some third
order sorting / boosting like you mentioned with "new music", or maybe
popularity or hotness to avoid quasi-random order. For events with limited
lifetime it's probably some mixture of spatial distance and freshness.

We will definetely keep an eye on the generation process of data for new
items. It depends on the domain but in the time of multi channel promotion
of videos, shows and products, it's also helps that there is traffic driven
from external sources.

Thanks for the detailed  hints - now it's time to see what comes out of


On Sun, Nov 12, 2017 at 7:52 AM, Ted Dunning <[EMAIL PROTECTED]> wrote: