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Recalculate item and user vectors on-the-fly
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def recalculate_user(user_ratings): | |
'''adds new user and its liked items to sparse matrix and returns recalculated recommendations''' | |
alpha = 40 | |
m = load_npz('sparse_user_item.npz') | |
n_users, n_movies = m.shape | |
ratings = [alpha for i in range(len(user_ratings))] | |
m.data = np.hstack((m.data, ratings)) | |
m.indices = np.hstack((m.indices, user_ratings)) | |
m.indptr = np.hstack((m.indptr, len(m.data))) | |
m._shape = (n_users+1, n_movies) | |
# recommend N items to new user | |
with open('model.sav', 'rb') as pickle_in: | |
model = pickle.load(pickle_in) | |
recommended, _ = zip(*model.recommend(n_users, m, recalculate_user=True)) | |
return recommended, map_movies(recommended) |
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