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I have a dataset like:

cid_int   item_id   score
  1         678      0.5
  2         787      0.6
  3         908      0.1
  .          .        .
  .          .        .

Now I'm running ALS model on this pyspark dataframe for getting recommendation using Collaborative Filtering.

als = ALS(userCol= "cid_int", itemCol= "item_id", ratingCol= "score", rank=5, maxIter=10, seed=0)
model = als.fit(X_train)

Now I have question that what does output of model.userFactors returns, does it return item embeddings like for m items I'll get all the embeddings?

And if yes can I use KNN on these embedding to find the closest items to given item?

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