tave-st commited on
Commit
9ca156e
1 Parent(s): 66cc316

typo in recsys

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Files changed (1) hide show
  1. recommender_system.py +3 -4
recommender_system.py CHANGED
@@ -7,7 +7,6 @@ from sklearn.manifold import TSNE
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  from os import cpu_count
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  import numpy as np
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  import time
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- import random
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  from utils import load_and_preprocess_data
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@@ -30,8 +29,8 @@ SIDEBAR_DESCRIPTION = """
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  ## What is it?
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  A recommender system is a tool that suggests something new to a particular
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- user that she/he might be interest in. It becomes really useful when
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- the number of items that a user can choose from is high.
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  ## How does it work?
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  A recommender system internally finds similar users and similar items,
@@ -44,7 +43,7 @@ already bought. If they are new and similar, the model suggests them.
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  ## How we prepare the data
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  For each user, we compute the quantity purchased for every single item.
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- This will be the metric the value considered by the modele to compute
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  the similarity. The item that a user has never bought will
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  be left at zero. These zeros will be the subject of the recommendation.
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  """.lstrip()
 
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  from os import cpu_count
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  import numpy as np
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  import time
 
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  from utils import load_and_preprocess_data
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  ## What is it?
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  A recommender system is a tool that suggests something new to a particular
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+ user that she/he might be interested in. It becomes useful when
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+ the number of items a user can choose from is high.
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  ## How does it work?
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  A recommender system internally finds similar users and similar items,
 
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  ## How we prepare the data
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  For each user, we compute the quantity purchased for every single item.
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+ This will be the metric the value considered by the model to compute
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  the similarity. The item that a user has never bought will
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  be left at zero. These zeros will be the subject of the recommendation.
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  """.lstrip()