why user-based recommendation for MovieLens 100k is very slow?

[Remy Meng]

the benchmarks (http://www.slideshare.net/marcelcaraciolo/crab-a-python-framework-for-building-recommender-systems page-37) 

Benchmarks Pure Python w/ Python w/ Scipy Dataset dicts and NumpyMovieLens 100k 15.32 s 9.56 s http://www.grouplens.org/node/73 Old Crab New Crab

however, in my case, I need more than 30 minutes to do it, I don't know why.

my code is

model = MatrixPreferenceDataModel(recommend_data.data)

similarity = UserSimilarity(model, pearson_correlation)

recommender = UserBasedRecommender(model, similarity, with_preference=True)

recommender.recommend("6")

[Raj_B]

I have downloaded the zip file of MovieLens 100k , after unzipping i found different files. Can you please help me which file i have to use and the required code to give that file as input?.... if i'm giving any file as,

from sys import argv

script, filename = argv

txt = open(filename)

movies = txt.read()

print movies

from scikits.crab.models import MatrixPreferenceDataModel

#Build the model

model = MatrixPreferenceDataModel(movies)

It is giving me following error....

Traceback (most recent call last):

  File "movieRecommender_UserBased_trail", line 18, in <module>

    model = MatrixPreferenceDataModel(movies)

  File "/usr/lib/python2.6/site-packages/scikits/crab/models/classes.py", line 88, in __init__

    self.build_model()

  File "/usr/lib/python2.6/site-packages/scikits/crab/models/classes.py", line 108, in build_model

    self._user_ids = np.asanyarray(self.dataset.keys())

AttributeError: 'str' object has no attribute 'keys'

[S244471@CTSINGMRVGHPC5 Crab_practice]$ vi movieRecommender_UserBased_trail

[Marcel Caraciolo]

Hi Raj and Remy I am still working on this new crab, the old one is deprecated I will need to put it offline. This is the new release:

github.com/python-recsys/crab

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[jeongseok choi]

I have same problem, I use movielens100k data.

The users ares 943, movies are 1682.

And I use python 2.7 in ubuntu and crab recommender.

I need more than 3 housr to get the result " recommender.recommend(2) "

Can you give some advice?