Updating model

[Martijn- Augment KU Leuven]

Hello

I implemented an ALS matrix factorisation recommender system with LensKit for my user study (see attachment)

Regarding this script I have 2 questions:

1) I wanted to use the recommend function to generate recommendations for only one user

https://lkpy.lenskit.org/en/stable/interfaces.html#lenskit.algorithms.Recommender

I tried this in my script

algoAls = als.BiasedMF(6)

fittableALS = util.clone(algoAls)

modelAls = fittableALS.fit(train)

rec = Recommender.adapt(modelAls)

recs = rec.recommend(firstUser, 10) #Gives error

But this was not working. Can anyone explain why?

(I solved the problem by using batch.recommend)

2) What is the best way to update the model if a new user enters?

    Do I fit the model again or are there more efficient ways to update only the user-feature matrix?

 

Thanks for the help!

Martijn

[Michael Ekstrand]

Martijn,

Could you provide the complete error message you are receiving? The code looks roughly correct, but the exact error message will shed light on the subject.

Right now ALS does not support any incremental retraining. In principle that is not difficult to do with ALS, but we haven't added thee code to do so. I will open an issue in the tracker to track this, in case anyone wants to add it :)

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Michael D. Ekstrand — michael...@boisestate.edu — https://md.ekstrandom.net
Assistant Professor, Dept. of Computer Science, Boise State University

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[Martijn- Augment KU Leuven]

If I use an int64 as input of the userId I got this error message:

Traceback (most recent call last):
  File "/Users/martijn/Documents/Doctoraat/Lenskit/WebRec/ALS.py", line 61, in <module>
    recs = rec.recommend(first, 10) #Gives error
  File "/Users/martijn/anaconda3/envs/condaEnv/lib/python3.7/site-packages/lenskit/algorithms/basic.py", line 281, in recommend
    scores = self.predictor.predict_for_user(user, candidates, ratings)
  File "/Users/martijn/anaconda3/envs/condaEnv/lib/python3.7/site-packages/lenskit/algorithms/als.py", line 227, in predict_for_user
    return self.score_by_ids(user, items)
  File "/Users/martijn/anaconda3/envs/condaEnv/lib/python3.7/site-packages/lenskit/algorithms/mf_common.py", line 102, in score_by_ids
    iidx = self.lookup_items(items)
  File "/Users/martijn/anaconda3/envs/condaEnv/lib/python3.7/site-packages/lenskit/algorithms/mf_common.py", line 68, in lookup_items
    return self.item_index_.get_indexer(items)
  File "/Users/martijn/anaconda3/envs/condaEnv/lib/python3.7/site-packages/pandas/core/indexes/base.py", line 3239, in get_indexer
    target = target.astype(object)
  File "/Users/martijn/anaconda3/envs/condaEnv/lib/python3.7/site-packages/pandas/core/indexes/base.py", line 1286, in astype
    return self.copy() if copy else self
  File "/Users/martijn/anaconda3/envs/condaEnv/lib/python3.7/site-packages/pandas/core/indexes/base.py", line 978, in copy
    new_index = self._shallow_copy()
  File "/Users/martijn/anaconda3/envs/condaEnv/lib/python3.7/site-packages/pandas/core/indexes/base.py", line 516, in _shallow_copy
    if not len(values) and 'dtype' not in kwargs:
TypeError: len() of unsized object

[Michael Ekstrand]

I suspect that the problem is a lack of a 'candidates' set, and the MF algorithms cannot currently produce a candidate list.

I'm working on this in https://github.com/lenskit/lkpy/issues/50.

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