(Feature) Recommend top N items given a user

[BradKML]

Querying every item on the table would be inefficient. There could be a way to make this process simpler by multiplying a user's vector with the whole item matrix.
Guessing: np.dot(self.pu_[u_ix], self.qi_) + self.bi_+ self.bu_[u_ix]

[gbolmier]

Great point @BrandonKMLee! We could either implement a new predict_n_item method or adapt the predict method to also handle this case. Feel free to open a pull request otherwise I'll do something later on.

[BradKML]

Another problem: identifying pre-rated items, to prevent bad recommendations.

[gbolmier]

Can you elaborate more on this, e.g. wdym by pre-rated items?

[BradKML]

@gbolmier when the rated items are in the dataset, recommendations should promote new unrated items over older viewed items (depends on the use case tho).

[gbolmier]

I was thinking of passing a list of the items' ids in parameter, so that it's easy to use as a mask on the item latent factor matrix

[BradKML]

Between masking the latent factors and masking the output, which is better?

[gbolmier]

If we mask the item latent factor matrix we'll only compute the estimated ratings of interest, which will save time compared to computing all the estimated ratings and then mask the ones we aren’t interested about.

[BradKML]

This would be more optimized!